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1.
Sensors (Basel) ; 24(7)2024 Apr 06.
Artigo em Inglês | MEDLINE | ID: mdl-38610550

RESUMO

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.


Assuntos
Atmosfera , Tecnologia de Sensoriamento Remoto , Estações do Ano , Biomassa , Biofísica , Nonoxinol
2.
Huan Jing Ke Xue ; 45(5): 2806-2816, 2024 May 08.
Artigo em Chinês | MEDLINE | ID: mdl-38629543

RESUMO

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.


Assuntos
Clima , Ecossistema , Humanos , Tecnologia de Sensoriamento Remoto , Areia , Carbono/análise , China , Mudança Climática
3.
Opt Lett ; 49(7): 1725-1728, 2024 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-38560847

RESUMO

Ultrasound coupling is one of the critical challenges for traditional photoacoustic (or optoacoustic) microscopy (PAM) techniques transferred to the clinical examination of chronic wounds and open tissues. A promising alternative potential solution for breaking the limitation of ultrasound coupling in PAM is photoacoustic remote sensing (PARS), which implements all-optical non-interferometric photoacoustic measurements. Functional imaging of PARS microscopy was demonstrated from the aspects of histopathology and oxygen metabolism, while its performance in hemodynamic quantification remains unexplored. In this Letter, we present an all-optical thermal-tagging flowmetry approach for PARS microscopy and demonstrate it with comprehensive mathematical modeling and ex vivo and in vivo experimental validations. Experimental results demonstrated that the detectable range of the blood flow rate was from 0 to 12 mm/s with a high accuracy (measurement error:±1.2%) at 10-kHz laser pulse repetition rate. The proposed all-optical thermal-tagging flowmetry offers an effective alternative approach for PARS microscopy realizing non-contact dye-free hemodynamic imaging.


Assuntos
Técnicas Fotoacústicas , Tecnologia de Sensoriamento Remoto , Técnicas Fotoacústicas/métodos , Reologia/métodos , Ultrassonografia/métodos , Microscopia/métodos
4.
Sci Rep ; 14(1): 8360, 2024 04 10.
Artigo em Inglês | MEDLINE | ID: mdl-38600271

RESUMO

Seagrasses are undergoing widespread loss due to anthropogenic pressure and climate change. Since 1960, the Mediterranean seascape lost 13-50% of the areal extent of its dominant and endemic seagrass-Posidonia oceanica, which regulates its ecosystem. Many conservation and restoration projects failed due to poor site selection and lack of long-term monitoring. Here, we present a fast and efficient operational approach based on a deep-learning artificial intelligence model using Sentinel-2 data to map the spatial extent of the meadows, enabling short and long-term monitoring, and identifying the impacts of natural and human-induced stressors and changes at different timescales. We apply ACOLITE atmospheric correction to the satellite data and use the output to train the model along with the ancillary data and therefore, map the extent of the meadows. We apply noise-removing filters to enhance the map quality. We obtain 74-92% of overall accuracy, 72-91% of user's accuracy, and 81-92% of producer's accuracy, where high accuracies are observed at 0-25 m depth. Our model is easily adaptable to other regions and can produce maps in in-situ data-scarce regions, providing a first-hand overview. Our approach can be a support to the Mediterranean Posidonia Network, which brings together different stakeholders such as authorities, scientists, international environmental organizations, professionals including yachting agents and marinas from the Mediterranean countries to protect all P. oceanica meadows in the Mediterranean Sea by 2030 and increase each country's capability to protect these meadows by providing accurate and up-to-date maps to prevent its future degradation.


Assuntos
Alismatales , Ecossistema , Humanos , Efeitos Antropogênicos , Mudança Climática , Inteligência Artificial , Tecnologia de Sensoriamento Remoto , Mar Mediterrâneo
5.
BMC Med Imaging ; 24(1): 86, 2024 Apr 10.
Artigo em Inglês | MEDLINE | ID: mdl-38600525

RESUMO

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4-5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.


Assuntos
Inteligência Artificial , Tecnologia de Sensoriamento Remoto , Humanos , Ciência de Dados , Armazenamento e Recuperação da Informação , Redes Neurais de Computação
6.
PLoS One ; 19(4): e0301444, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38626150

RESUMO

Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods.


Assuntos
Ecossistema , Imagens de Satélites , Imagens de Satélites/métodos , Pradaria , Tecnologia de Sensoriamento Remoto/métodos , China
7.
PLoS One ; 19(4): e0298098, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38573975

RESUMO

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, this paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the Latent Dirichlet Allocation (LDA) topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil unmanned aerial vehicles (UAVs) as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the data acquisition, main equipment, and ground platform intelligence.


Assuntos
Tecnologia Disruptiva , Tecnologia , Tecnologia de Sensoriamento Remoto/métodos
8.
PLoS One ; 19(4): e0297027, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38564609

RESUMO

Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn (Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha-1) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha-1) between furrow-applied slow-release fertilizer at ≥156 kg N ha-1 and 208 kg N ha-1 side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.


Assuntos
Fertilizantes , Zea mays , Grão Comestível , Tecnologia de Sensoriamento Remoto/métodos , Ureia
9.
PLoS One ; 19(4): e0288121, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38568890

RESUMO

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.


Assuntos
Animais Selvagens , Aprendizado Profundo , Animais , Fluxo de Trabalho , Redes Neurais de Computação , Tecnologia de Sensoriamento Remoto/métodos , Aves
10.
Environ Monit Assess ; 196(4): 401, 2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38538854

RESUMO

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI's additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the -chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.


Assuntos
Monitoramento Ambiental , Tecnologia de Sensoriamento Remoto , Clorofila A , Monitoramento Ambiental/métodos , Clorofila/análise , Água
11.
J Environ Manage ; 356: 120542, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38492424

RESUMO

Urban trees have attracted increasing attention to serve as a green prescription for addressing various challenges facing human society like climate change and environmental deterioration. However, without healthy growth of urban trees, they cannot service any environmental, social, and economic benefits in a sustainable manner. By monitoring the canopy development, the tree growth dynamics in different urban habitats can be detected and appropriate management approaches can be executed. Using the Kowloon Peninsula, Hong Kong, as a case, this study explores how remote sensing data can help monitor and understand the impacts of heterogeneous urban habitats on tree canopy dynamics. Four algorithms based on WorldView-2 satellite image are compared to optimize the canopy segmentation. Then the individual tree canopy is integrated with Sentinel-2 satellite data to obtain canopy growth dynamics for each season from 2016 to 2020. Three indicators are applied to reflect tree canopy status, including the fluorescence correction vegetation index (FCVI, tracking leaf chlorophyll density), the soil adjusted total vegetation index (SATVI, measuring the density of woody branches and twigs), and the normalised difference phenology index (NDPI, capturing canopy water content). And four heterogeneous habitats where urban trees stand are specified. The results revealed that urban trees show varying canopy growth status, in a descending order from natural terrains, parks, residential lands, to road verges, suggesting that urban habitats curtail trees' growth significantly. Additionally, two super-typhoons in 2017 and 2018, respectively, caused serious damages to tree canopy. Relevant resiliency of tree varies, echoing the sequence of canopy growth status with those in road verges the least resilient. This study shows how remote sensing data can be used to provide a better understanding of long-term tree canopy dynamics across large-scale heterogeneous urban habitats, which is key to monitoring and maintaining the health and growth of urban trees.


Assuntos
Tecnologia de Sensoriamento Remoto , Árvores , Humanos , Estudos Longitudinais , Ecossistema , Solo
12.
Environ Monit Assess ; 196(4): 377, 2024 Mar 18.
Artigo em Inglês | MEDLINE | ID: mdl-38499899

RESUMO

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlidere, and Elmali), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.


Assuntos
Água Potável , Tecnologia de Sensoriamento Remoto , Recursos Hídricos , Monitoramento Ambiental/métodos , Urbanização
13.
Environ Monit Assess ; 196(4): 338, 2024 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-38430346

RESUMO

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.


Assuntos
Inundações , Sistemas de Informação Geográfica , Processo de Hierarquia Analítica , Monitoramento Ambiental/métodos , Tecnologia de Sensoriamento Remoto
14.
Kardiol Pol ; 82(3): 308-314, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38493457

RESUMO

BACKGROUND: Remote monitoring (RM) of cardiac implantable electronic devices for adults offers improved treatment efficacy and, consequently, better patient clinical outcomes. There is scant data on the value and prognosis of RM in the pediatric population. AIMS: The goal of this study was to determine the efficacy of RM by analyzing the connectivity of bedside transmitters, adherence to planned automatic follow-ups, and occurrence of alert-based events. METHODS: We evaluated the pediatric population with implanted pacemakers for congenital AV block or after surgically corrected congenital heart diseases. RESULTS: A total of 69 patients were included in our study. The median (Q1-Q3) patient age was 6.0 (2.0-11.0) years. All patients received bedside transmitters and were enrolled in the RM system. Among them, 95.7% of patients had their first scheduled follow-up successfully sent. Patients were followed up remotely over a median time of 33.0 (13-45) months. Only 42% of patients were continuously monitored, and all scheduled transmissions were delivered on time. Further analysis revealed that 34.8% of patients missed transmissions between June and September (holiday season). Alert-based events were observed in 40.6% patients, mainly related to epicardial lead malfunction and arrhythmic events. Overall compliance was also compromised by socioeconomic factors. CONCLUSIONS: Our findings are in concordance with recently published results by PACES regarding a high level of compliance in patient enrollment to RM and time to initial transmission. However, a lower level of adherence was observed during the holiday season due to interrupted connectivity of bedside transmitters. Importantly, a relatively low occurrence of alert transmissions was observed, mainly related to epicardial lead malfunction and arrhythmic events.


Assuntos
Desfibriladores Implantáveis , Marca-Passo Artificial , Adulto , Humanos , Criança , Tecnologia de Sensoriamento Remoto/métodos , Monitorização Fisiológica/métodos , Arritmias Cardíacas/terapia
15.
Neural Netw ; 174: 106241, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38508050

RESUMO

Remarkable achievements have been made in the field of remote sensing cross-scene classification in recent years. However, most methods directly align the entire image features for cross-scene knowledge transfer. They usually ignore the high background complexity and low category consistency of remote sensing images, which can significantly impair the performance of distribution alignment. Besides, shortcomings of the adversarial training paradigm and the inability to guarantee the prediction discriminability and diversity can also hinder cross-scene classification performance. To alleviate the above problems, we propose a novel cross-scene classification framework in a discriminator-free adversarial paradigm, called Adversarial Pair-wise Distribution Matching (APDM), to avoid irrelevant knowledge transfer and enable effective cross-domain modeling. Specifically, we propose the pair-wise cosine discrepancy for both inter-domain and intra-domain prediction measurements to fully leverage the prediction information, which can suppress negative semantic features and implicitly align the cross-scene distributions. Nuclear-norm maximization and minimization are introduced to enhance the target prediction quality and increase the applicability of the source knowledge, respectively. As a general cross-scene framework, APDM can be easily embedded with existing methods to boost the performance. Experimental results and analyses demonstrate that APDM can achieve competitive and effective performance on cross-scene classification tasks.


Assuntos
Conhecimento , Tecnologia de Sensoriamento Remoto , Semântica
16.
Sci Rep ; 14(1): 5472, 2024 03 05.
Artigo em Inglês | MEDLINE | ID: mdl-38443548

RESUMO

Understanding the response of salt marshes to flooding is crucial to foresee the fate of these fragile ecosystems, requiring an upscaling approach. In this study we related plant species and community response to multispectral indices aiming at parsing the power of remote sensing to detect the environmental stress due to flooding in lagoon salt marshes. We studied the response of Salicornia fruticosa (L.) L. and associated plant community along a flooding and soil texture gradient in nine lagoon salt marshes in northern Italy. We considered community (i.e., species richness, dry biomass, plant height, dry matter content) and individual traits (i.e., annual growth, pigments, and secondary metabolites) to analyze the effect of flooding depth and its interplay with soil properties. We also carried out a drone multispectral survey, to obtain remote sensing-derived vegetation indices for the upscaling of plant responses to flooding. Plant diversity, biomass and growth all declined as inundation depth increased. The increase of soil clay content exacerbated flooding stress shaping S. fruticosa growth and physiological responses. Multispectral indices were negatively related with flooding depth. We found key species traits rather than other community traits to better explain the variance of multispectral indices. In particular stem length and pigment content (i.e., betacyanin, carotenoids) were more effective than other community traits to predict the spectral indices in an upscaling perspective of salt marsh response to flooding. We proved multispectral indices to potentially capture plant growth and plant eco-physiological responses to flooding at the large scale. These results represent a first fundamental step to establish long term spatial monitoring of marsh acclimation to sea level rise with remote sensing. We further stressed the importance to focus on key species traits as mediators of the entire ecosystem changes, in an ecological upscaling perspective.


Assuntos
Ecossistema , Áreas Alagadas , Tecnologia de Sensoriamento Remoto , Aclimatação , Solo
17.
Environ Monit Assess ; 196(4): 353, 2024 Mar 11.
Artigo em Inglês | MEDLINE | ID: mdl-38466443

RESUMO

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30 cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.


Assuntos
Tecnologia de Sensoriamento Remoto , Solo , Irã (Geográfico) , Monitoramento Ambiental/métodos , Modelos Lineares
18.
Proc Biol Sci ; 291(2018): 20232067, 2024 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-38471550

RESUMO

Like many polar animals, emperor penguin populations are challenging to monitor because of the species' life history and remoteness. Consequently, it has been difficult to establish its global status, a subject important to resolve as polar environments change. To advance our understanding of emperor penguins, we combined remote sensing, validation surveys and using Bayesian modelling, we estimated a comprehensive population trajectory over a recent 10-year period, encompassing the entirety of the species' range. Reported as indices of abundance, our study indicates with 81% probability that there were fewer adult emperor penguins in 2018 than in 2009, with a posterior median decrease of 9.6% (95% credible interval (CI) -26.4% to +9.4%). The global population trend was -1.3% per year over this period (95% CI = -3.3% to +1.0%) and declines probably occurred in four of eight fast ice regions, irrespective of habitat conditions. Thus far, explanations have yet to be identified regarding trends, especially as we observed an apparent population uptick toward the end of time series. Our work potentially establishes a framework for monitoring other Antarctic coastal species detectable by satellite, while promoting a need for research to better understand factors driving biotic changes in the Southern Ocean ecosystem.


Assuntos
Spheniscidae , Animais , Ecossistema , Teorema de Bayes , Fatores de Tempo , Tecnologia de Sensoriamento Remoto , Regiões Antárticas
19.
PLoS One ; 19(3): e0285912, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38527020

RESUMO

Remote sensing of spectral reflectance is a crucial parameter in precision agriculture. In particular, the visual color produced from reflected light can be used to determine plant health (VIS-IR) or attract pollinators (Near-UV). However, the UV spectral reflectance studies largely focus on non-crop plants, even though they provide essential information for plant-pollinator interactions. This literature review presents an overview of UV-reflectance in crops, identifies gaps in the literature, and contributes new data based on strawberry cultivars. The study found that most crop spectral reflectance studies relied on lab-based methodologies and examined a wide spectral range (Near UV to IR). Moreover, the plant family distribution largely mirrored global food market trends. Through a spectral comparison of white flowering strawberry cultivars, this study discovered visual differences for pollinators in the Near UV and Blue ranges. The variation in pollinator visibility within strawberry cultivars underscores the importance of considering UV spectral reflectance when developing new crop breeding lines and managing pollinator preferences in agricultural fields.


Assuntos
Fragaria , Tecnologia de Sensoriamento Remoto , Melhoramento Vegetal , Produtos Agrícolas
20.
Huan Jing Ke Xue ; 45(3): 1586-1597, 2024 Mar 08.
Artigo em Chinês | MEDLINE | ID: mdl-38471872

RESUMO

The ecological environment along the Qinghai-Xizang highway is an important part of the construction of the ecological civilization in the Xizang region, and current research generally suffers from difficulties in data acquisition, low timeliness, and failure to consider the unique "alpine saline" environmental conditions in the study area due to the unique geographical environment of the Qinghai-Xizang plateau. Based on the GEE platform and the unique geographical environment of the study area, the remote sensing ecological index (RSEI) was improved, and a new saline remote sensing ecological index (SRSEI) applicable to the alpine saline region was constructed by using principal component analysis as an ecological environment quality evaluation index. The spatial distribution pattern and temporal variation trend of ecological environment quality along the Qinghai-Xizang Highway Nagqu-Amdo section were analyzed at multiple spatial and temporal scales using the ArcGIS 10.3 platform and geographic probes, and the driving mechanisms of eight control factors, including natural and human-made, on the spatial and temporal changes in SRSEI were investigated. The results showed that:① compared with RSEI, SRSEI was more sensitive to vegetation and had a stronger discriminatory ability in areas with sparse vegetation and severe salinization, which is suitable for ecological quality evaluation in alpine saline areas. ② The spatial scale of ecological environment quality in the study area had obvious geographical differentiation, and the areas with poor ecological quality were mainly concentrated in the northern Amdo County, whereas the areas with excellent and good quality grades were mainly distributed in the central-western and southeastern Nagqu areas. On the temporal scale, the ecological environment of the study area as a whole showed an improvement trend over 32 years, and the vegetation cover in the central-western and southeastern areas increased significantly, which had a strong improvement effect on the ecological environment. The improvement area was 1 425.98 km2, accounting for 99.82%. The mean value of SRSEI was 0.49, with an overall fluctuating upward trend and an average increase of 0.015 7 a-1. ③ The land use pattern was the most driving influence factor in the change of ecological environment quality in the study area, with an average q value of 0.157 6 over multiple years, and the influence of environmental factors was low. The multi-factor interaction results showed that the ecological environment in the study area was the result of multiple factors acting together, all factors had synergistic enhancement under the interaction, the influence of human factors was gradually increasing, and the interaction of the net primary productivity (NPP) of vegetation and land use pattern was the main interactive control factor of ecological environment quality in the study area. This study can provide a theoretical basis for ecological environmental protection and sustainable development along the Nagqu to Amdo section.


Assuntos
Ecossistema , Tecnologia de Sensoriamento Remoto , Humanos , Monitoramento Ambiental , Conservação dos Recursos Naturais , Análise de Componente Principal , China
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