Spatio-temporal analysis and prediction of land use land cover (LULC) change in Wular Lake, Jammu and Kashmir, India.

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.

Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing.

Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-learning processes harnessing big data, including remote sensing, can offer a new era of decision-support tools (DST) for pasture monitoring. Its application on-farm remains poor because of a lack of evidence about its accuracy. This study aimed at evaluating and quantifying the minimum data required to train a machine-learning satellite-based DST focusing on accurate pasture biomass prediction using this approach. Management data from 14 farms in New South Wales, Australia and measured pasture biomass throughout 12 consecutive months using a calibrated rising plate meter (RPM) as well as pasture biomass estimated using a DST based on high temporal/spatial resolution satellite images were available. Data were balanced according to farm and week of each month and randomly allocated for model evaluation (20%) and for progressive training (80%) as follows: 25% training subset (1W: week 1 in each month); 50% (2W: week 1 and 3); 75% (3W: week 1, 3, and 4); and 100% (4W: week 1 to 4). Pasture biomass estimates using the DST across all training datasets were evaluated against a calibrated rising plate meter (RPM) using mean-absolute error (MAE, kg DM/ha) among other statistics. Tukey's HSD test was used to determine the differences between MAE across all training datasets. Relative to the control (no training, MAE: 498 kg DM ha-1) 1W did not improve the prediction accuracy of the DST (P > 0.05). With the 2W training dataset, the MAE decreased to 342 kg DM ha-1 (P < 0.001), while for the other training datasets, MAE decreased marginally (P > 0.05). This study accounts for minimal training data for a machine-learning DST to monitor pastures from satellites with comparable accuracy to a calibrated RPM which is considered the 'gold standard' for pasture biomass monitoring.

Assessing the accuracy of spectral indices obtained from Sentinel images using field research to estimate land degradation.

Wind erosion resulting from soil degradation is a significant problem in Iran's Baluchistan region. This study evaluated the accuracy of remote sensing models in assessing degradation severity through field studies. Sentinel-2 Multispectral Imager's (MSI) Level-1C satellite data was used to map Rutak's degradation severity in Saravan. The relationship between surface albedo and spectral indices (NDVI, SAVI, MSAVI, BSI, TGSI) was assessed. Linear regression establishes correlations between the albedo and each index, producing a degradation severity map categorized into five classes based on albedo and spectral indices. Accuracy was tested with 100 ground control points and field observations. The Mann-Whitney U-Test compares remote sensing models with field data. Results showed no significant difference (P > 0.05) between NDVI, SAVI, and MSAVI models with field data, while BSI and TGSI models exhibited significant differences (P ≤ 0.001). The best model, BSI-NDVI, achieves a regression coefficient of 0.86. This study demonstrates the advantage of remote sensing technology for mapping and monitoring degraded areas, providing valuable insights into land degradation assessment in Baluchistan. By accurately identifying severity levels, informed interventions can be implemented to mitigate wind erosion and combat soil degradation in the region.

Intra-annual vegetation changes and spatial variation in China over the past two decades based on remote sensing and time-series clustering.

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16 days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies.

Submersible voltammetric sensing probe for rapid and extended remote monitoring of opioids in community water systems.

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

Integrated monitoring and prediction of thermal discharge from nuclear power plants using satellite, UAV, and numerical simulation.

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

Dispersion analysis of the 2017 Persian Gulf oil spill based on remote sensing data and numerical modelling.

Oil spills, detected by SAR sensors as dark areas, are highly effective marine pollutants that affect the ocean surface. These spills change the water surface tension, attenuating capillary gravitational waves and causing specular reflections. We conducted a case study in the Persian Gulf (Arabian Sea to the Strait of Hormuz), where approximately 163,900 gal of crude oil spilled in March 2017. Our study examined the relationship between oil weathering processes and extracted backscatter values using zonal slices projected over SAR-detected oil spills. Internal backscatter values ranged from -22.5 to -23.5, indicating an oil chemical binding and minimal interaction with seawater. MEDSLIK-II simulations indicated increased oil solubilization and radar attenuation rates with wind, facilitating coastal dispersion. Higher backscatter at the spill edges compared to the core reflected different stages of oil weathering. These results highlight the complex dynamics of oil spills and their environmental impact on marine ecosystems.

Classification of major species in the sericite-Artemisia desert grassland using hyperspectral images and spectral feature identification.

Background: The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring. Methods: Ground hyperspectral images of a sericite-Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land. Results: The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%). Conclusions: The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite-Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique.

Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the input point cloud data. The machine learning-based methods for segmenting point clouds suffer from the imprecise computation of the training feature values. The most important factor that influences how precisely the feature values are computed is the neighborhood chosen by each point. This research addresses this issue and proposes a suitable adaptive neighborhood selection approach for individual points by completely considering the complex and heterogeneous nature of the input LiDAR point cloud data. The proposed approach is evaluated on high-density mobile and low-density aerial LiDAR point cloud datasets using the Random Forest machine learning classifier. In the context of performance evaluation, the proposed approach confirms the competitive performance over the state-of-the-art approaches. The computed accuracy and F1-score for the high-density Toronto and low-density Vaihingen datasets are greater than 91% and 82%, respectively.

A new two-step road extraction method in high resolution remote sensing images.

High-resolution remote sensing technology is an efficient and low-cost space-to-earth observation strategy, which can carry out simultaneous monitoring of large-scale areas. It has incomparable advantages over ground monitoring solutions. Traditional road extraction methods are mainly based on image processing techniques. These methods usually only use one or a few features of images, which is difficult to fully deal with the real situation of roads. This work proposes a two-steps network for the road extraction. First, we optimize a pix2pix model for image translation to obtain the required map style image. Images output by the optimized model is full of road features and can relief the occlusion issues. It can intuitively reflect information such as the position, shape and size of the road. After that, we propose a new FusionLinkNet model, which has a strong stability in the road information by fusing the DenseNet, ResNet and LinkNet. Experiments show that our accuracy and learning rate have been improved. The MIOU (Mean Intersection Over Union) value of the proposed model in road extraction is over 80% in both DeepGlobe and Massachusetts road dataset. The figures are available from https://github.com/jsit-luwei/training-dataset.

Satellite observations reveal anthropogenic pressure significantly affects the suspended particulate matter concentrations in coastal waters of Hainan Island.

Suspended particulate matter (SPM) plays a crucial role in assessing the health status of coastal ecosystems. Satellite remote sensing offers an effective approach to investigate the variations and distribution patterns of SPM, with the performance of various satellite retrieval models exhibiting significant spatial heterogeneity. However, there is still limited information on precise remote sensing retrieval algorithms specifically designed for estimating SPM in tropical areas, hindering our ability to monitor the health status of valuable tropical ecological resources. A relatively accurate empirical algorithm (root mean square error = 2.241 mg L-1, mean absolute percentage error = 42.527%) was first developed for the coastal SPM of Hainan Island based on MODIS images and over a decade of field SPM data, which conducted comprehensive comparisons among empirical models, semi-analytical models, and machine learning models. Long-term monitoring from 2003 to 2022 revealed that the average SPM concentration along the coastal wetlands of Hainan Island was 6.848 mg L-1, which displayed a decreasing trend due to government environmental protection regulations (average rate of change of -0.009 mg L-1/year). The seasonal variations in coastal SPM were primarily influenced by sea surface temperature (SST). Spatially, the concentrations of SPM along the southwest coast of Hainan Island were higher in comparison to other waters, which was attributable to sediment types and ocean currents. Further, anthropogenic pressure (e.g., agricultural waste input, vegetation cover) was the main influence on the long-term changes of coastal SPM in Hainan Island, particularly evident in typical tropical ecosystems affected by aquaculture, coastal engineering, and changes in coastal green vegetation. Compared to other typical ecosystems around the globe, the overall health status of SPM along the coast wetlands of Hainan is considered satisfactory. These findings not only establish a robust remote sensing model for long-term SPM monitoring along the coast of Hainan Island, but also provide comprehensive insights into SPM dynamics, thereby contributing to the formulation of future coastal zone management policies.

Assessing the Presence of Pithomyces chartarum in Pastureland Using IoT Sensors and Remote Sensing: The Case Study of Terceira Island (Azores, Portugal).

Spores from the fungus Pithomyces chartarum are commonly found on Azorean pastures. When consumed by cattle along with the grass, these spores cause health issues in the cattle, resulting in animal suffering and financial losses. For approximately two years, we monitored meteorological parameters using weather stations and collected and analyzed grass samples in a laboratory to control for the presence of spores. The data confirmed a connection between meteorology and sporulation, enabling the prediction of sporulation risk. To detect the presence of spores in pastures rather than predict it, we employed field spectrometry and Sentinel-2 reflectance data to measure the spectral signatures of grass while controlling for spores. Our findings indicate that meteorological variables from the past 90 days can be used to predict sporulation, which can enhance the accuracy of a web-based alert system used by farmers to manage the risk. We did not detect significant differences in spectral signatures between grass with and without spores. These studies contribute to a deeper understanding of P. chartarum sporulation and provide actionable information for managing cattle, ultimately improving animal welfare and reducing financial losses.

Using Data Augmentation to Improve the Generalization Capability of an Object Detector on Remote-Sensed Insect Trap Images.

Traditionally, monitoring insect populations involved the use of externally placed sticky paper traps, which were periodically inspected by a human operator. To automate this process, a specialized sensing device and an accurate model for detecting and counting insect pests are essential. Despite considerable progress in insect pest detector models, their practical application is hindered by the shortage of insect trap images. To attenuate the "lack of data" issue, the literature proposes data augmentation. However, our knowledge about data augmentation is still quite limited, especially in the field of insect pest detection. The aim of this experimental study was to investigate the effect of several widely used augmentation techniques and their combinations on remote-sensed trap images with the YOLOv5 (small) object detector model. This study was carried out systematically on two different datasets starting from the single geometric and photometric transformation toward their combinations. Our results show that the model's mean average precision value (mAP50) could be increased from 0.844 to 0.992 and from 0.421 to 0.727 on the two datasets using the appropriate augmentation methods combination. In addition, this study also points out that the integration of photometric image transformations into the mosaic augmentation can be more efficient than the native combination of augmentation techniques because this approach further improved the model's mAP50 values to 0.999 and 0.756 on the two test sets, respectively.

A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.

The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

Application of remote sensing to understand the role of Galician feral horses in the biomass reduction of a shrub-grassland-dominated landscape.

Galician forests in northwestern Spain are subject to frequent wildfires with high environmental and economic costs. In addition, due to the consequences of climate change, these fires are becoming more virulent, occurring throughout the year, and taking place in populated areas, in some cases involving the loss of human life. Therefore, forest fire prevention is even more relevant than mitigating its consequences. Given the costs involved in forestry work, alternative measures to reduce fuel load and create vegetation gaps are needed. One involves grazing by an endemic species of feral horses (Equus ferus atlanticus) that feed on thicket-forming gorse (Ulex europaeus). In a 100-ha forest fenced study area stocked with 11 horses, four 50 m2 enclosed plots prevented the access of these wild animals to the vegetation, with the aim of manipulating their impact on the reduction of forest biomass. The measurement of biomass volumes is an important method that can describe the assessment of wildfire risks, unfortunately, high-resolution data collection at the regional scale is very time-consuming. The best result can be using drones (unmanned aerial vehicles - UAVs) as a method of collecting remotely sensed data at low cost. From September 2018 to November 2020, we collected information about aboveground biomass from these four enclosed plots and their surrounding areas available for horses to forage, via UAV. These data, together with environmental variables from the study site, were used as input for a fire model to assess the differences in the surface rate of spread (SROS) among grazed and ungrazed areas. Our results indicated a consistent but small reduction in the SROS between 0.55 and 3.10 m/min in the ungrazed enclosured plots in comparison to their grazed surrounding areas (which have an SROS between 15 and 25 m/min). The research showed that radar remote sensing (UAV) can be used to map forest aboveground biomass, and emphasized the importance and role of feral horses in Galicia as a prevention tool against wildfires in gorse-dominated landscapes.

Disturbance and vegetational structure in an urban forest of Indian Siwaliks: an ecological assessment.

Urban forests face multiple human-mediated pressures leading to compromised ecosystem structure and functioning. Therefore, understanding ecosystem structure in response to ongoing pressures is crucial for sustaining ecological integrity and human well-being. We aim to assess the disturbance and its effects on the vegetation structure of urban forests in Chandigarh using a combination of remote sensing techniques and vegetation surveys. The disturbance was evaluated as a change in NDVI (Normalised Difference Vegetation Index) from 2001 to 2021 by applying the BFAST (Breaks For Additive Season and Trend) algorithm to the MODIS satellite imagery data. A vegetation survey was conducted to compare the species composition, taxonomic and phylogenetic diversity as measures of forest vegetational structure. While signals of disturbance were evident, the changes in vegetation structure were not well established from our study. Further, this analysis indicated no significant differences in vegetation composition due to disturbance (F1,12 = 0.91, p = 0.575). However, the phylogenetic diversity was substantially lower for disturbed plots than undisturbed plots, though the taxonomic diversity was similar among the disturbed and undisturbed plots. Our results confirmed that disturbance effects are more prominent on the phylogenetic than taxonomic diversity. These findings can be considered early signals of disturbance and its impact on the vegetation structure of urban forests and contribute to the knowledge base on urban ecosystems. Our study has implications for facilitating evidence-based decision-making and the development of sustainable management strategies for urban forest ecosystems.

Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States.

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Trebon fishponds, Czech Republic.

Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Trebon fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022-2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svet fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 µg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Nadeje, Svet, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 µg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Trebon region.