Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.

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.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal.

PREMISE: Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. METHODS: Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. RESULTS: Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. CONCLUSIONS: Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

The rectangular tile classification model based on Sentinel integrated images enhances grassland mapping accuracy: A case study in Ordos, China.

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.

Thermal-tagging photoacoustic remote sensing flowmetry.

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.

Integrating plant morphological traits with remote-sensed multispectral imageries for accurate corn grain yield prediction.

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.

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

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.

Research on potential disruptive technology identification based on technology network.

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.

Phenological characteristics of net ecosystem carbon exchange of Stipa krylovii steppe in Inner Mongolia, China and its remote sensing monitoring.

To accurately monitor the phenology of net ecosystem carbon exchange (NEE) in grasslands with remote sensing, we analyzed the variations in NEE and its phenology in the Stipa krylovii steppe and discussed the remote sensing vegetation index thresholds for NEE phenology, with the observational data from the Inner Mongolia Xilinhot National Climate Observatory's eddy covariance system and meteorological gradient observation system during 2018-2021, as well as Sentinel-2 satellite data from January 1, 2018 to December 31, 2021. Results showed that, from 2018 to 2021, NEE exhibited seasonal variations, with carbon sequestration occurring from April to October and carbon emission in other months, resulting in an overall carbon sink. The average Julian days for the start date (SCUP) and the end date (ECUP) of carbon uptake period were the 95th and 259th days, respectively, with an average carbon uptake period lasting 165 days. Photosynthetically active radiation showed a negative correlation with daily NEE, contributing to carbon absorption of grasslands. The optimal threshold for capturing SCUP was a 10% threshold of the red-edge chlorophyll index, while the normalized difference vegetation index effectively reflected ECUP with a threshold of 75%. These findings would provide a basis for remote sensing monitoring of grassland carbon source-sink dynamics.

Correlations between ecosystem service value and landscape ecological risk and its spatial heterogeneity in Jilin Province, China.

Exploring the correlations between ecosystem service value (ESV) and landscape ecological risk and the driving factors of their spatial variations is crucial for maintaining regional ecological security and promoting sustainable human well-being. We carried out a grid resampling size of 5 km×5 km assessment units of Jilin Pro-vince based on the remote sensing monitoring data of land use in 2000, 2005, 2010, 2015, and 2020. We quantitatively evaluated the landscape ecological risk and ESV, and analyzed their spatial-temporal variations. Employing bivariate spatial autocorrelation analysis and the geographical detector models, we examined the correlation between the landscape ecological risk and ESV and explored the driving factors for their spatial variations. The results showed that ESV in Jilin Province decreased from 385.895 billion yuan to 378.211 billion yuan during 2000-2020. The eastern region was dominated by extremely low risk, medium risk, and low risk areas. In contrast, the western region was mainly composed of extremely high risk and high risk areas. There was a significant negative correlation and spatial negative correlation between landscape ecological risk and ESV in Jilin Province. Human activity and land use type were the important driving factors for spatial differentiation in both landscape ecological risk and ESV. Our findings suggested that scientific land use regulation and appropriate control of human activities are critically needed to optimize Jilin Province's ecological environment.

AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts.

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.

[Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021].

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.

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits.

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.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

[Ecological Environment Assessment and Driving Mechanism Analysis of Nagqu and Amdo Sections of Qinghai-Xizang Highway Based on Improved Remote Sensing Ecological Index].

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.

Long-term remote sensing-based methods for monitoring air pollution and cloud cover in the Balkan countries.

The use of remote sensing and GIS methodology has accelerated the processing of data on pollution, but has also raised a question about the accuracy of the same. The research focuses on four main air pollutants (CO, NO, SO2, O3), the data on which were obtained from satellite images of Landsat 8 and Landsat 9, for the period 2000-2020. The data on relative cloudiness were obtained from the database CHELSA (Climatologies at high resolution for the earth's land surface areas) for the period 1980-2010. All the data were further processed and analyzed through the procedures of numerical GIS analysis, multi-criteria analysis, supervised and unsupervised satellite classification, and pixel analysis. The results of the analysis of cloud cover in the Balkan region showed that the month with the highest cloud cover in this period was February, with the maximum of (93.18%), whereas the lowest cloud cover was in July (0.19%). The analyzed period (2000-2010) was in the middle range for the pollutants NO and SO2 and in the lower range for CO; O3. In the period 2010-2020, there were high concentrations of NO, SO2, and CO and low concentrations of O3. The most polluted cities in the last twenty years are Ordu (Turkey), Sarajevo (Bosnia and Herzegovina), and Bor (Serbia). Finally, two most extreme air pollutants in the territory of Balkan countries were SO2 and NO (2000-2020).

New perspective for the upscaling of plant functional response to flooding stress in salt marshes using remote sensing.

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.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir.

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.

Advances in remote sensing of emperor penguins: first multi-year time series documenting trends in the global population.

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.

Pacemaker remote monitoring challenges in pediatric population: Single center long term experience.

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.