Spatial Distribution Maps Research Articles

Geolocation of Fixed Emission Sources Produced by Artisan Brick Kilns in the Atmospheric Basin of the City of Juliaca, Peru

Objective: The study objective is to investigate the application of georeferencing systems to identify the geospatial location of fixed sources of atmospheric emissions produced by artisanal brickyards in the air basin of Juliaca city-Peru. Theoretical Framework: Previous studies have shown that artisanal brickyards are a significant source of air pollution in developing urban and on the outskirts. Emissions of fine particles, nitrogen oxides and other pollutants from the burning of traditional fuels, such as firewood and coal contribute to the degradation of air quality and can have adverse effects on human health and the environment. The georeferencing application and geospatial analysis techniques in the atmospheric pollution study have allowed a better understanding of the spatial distribution of emission sources and their impact on the environment. These tools are essential to identify and map the artisanal brickyard locations and to evaluate their contribution to air pollution in urban and on the outskirts. Method: The methodology adopted for this research includes data collection and compilation of existing information; georeferencing of brickyards through the use of geographic information systems (GIS); analysis of geospatial data for the identification of spatial patterns; preparation of thematic and spatial distribution maps; and the interpretation of results. Results and Discussion: The study results highlight the importance of locating the fixed sources of emissions produced by artisanal brick factories in the Juliaca air basin. This precise spatial identification provides a solid basis for the formulation of policies and mitigation strategies aimed at reducing air pollution in the region, in addition to representing fundamental data for the use and exploitation of GIS for environmental protection and modelling, among others. Research Implications: The results can be applied or influence practices in the environmental and forestry engineering field, the ICTs application, modelling and simulation, and territorial planning, among others. Originality/Value: This study contributes to the literature using georeferencing techniques for environmental modelling purposes. The relevance and value of this research are evident in obtaining georeferenced points for the estimation of emission factors for the predictive calculation of volumes, flows and dispersion of pollutants in an air basin.

TMET-23. MASS SPECTROMETRY-BASED METABOLIC PROFILING AND IMAGING OF ASTROCYTE AND GLIOBLASTOMA INVASIVE MARGIN CELLS

Abstract Despite multimodal treatment, 90% of patients diagnosed with isocitrate dehydrogenase (IDH) wild-type glioblastoma experience tumour recurrence within 5 years. Poor prognosis is in considerable part attributed to the ability of cancer cells to infiltrate a healthy brain. We hypothesise that metabolic changes underpinning cellular crosstalk between astrocytes and glioblastoma cells isolated from the invasive margin within a physiologically representative in-vitro tumour microenvironment may reveal therapeutic targets to prevent or delay glioblastoma recurrence. This study used advanced analytical techniques to establish baseline metabolomic profiles for eGFP-tagged primary glioblastoma cells isolated from the invasive margin and healthy human cortical astrocytes. Liquid chromatography-mass spectrometry (LC-MS) enabled detailed analysis of biochemical pathways, and Atmospheric Pressure-Matrix Assisted Laser Desorption/Ionisation (AP-MALDI) imaging offered spatial localization of metabolites, critical for mapping their distribution within tumour microenvironments. Metabolites were extracted using a biphasic method (methanol:chloroform:water) for LC-MS analysis, and metabolic profiling was conducted using a ZIC-pHILIC column coupled with a Q-Exactive Orbitrap Mass Spectrometer. Additionally, AP-MALDI imaging facilitated the generation of high-resolution spatial distribution maps of selected metabolites, achieving a resolution of 10 µm. Both multivariate and univariate statistical analyses were applied to identify significant metabolic alterations. Metabolic profiling identified distinct pathways altered in glioblastoma cells compared to astrocytes, including alterations in aspartate, glycine and serine, and lipid metabolism. High-resolution imaging outlined the spatial distribution of metabolites involved in these pathways, revealing regions of metabolic heterogeneity. Furthermore, this methodology facilitated an expanded identification range of essential biomolecules, notably lipids and amino acids, thereby augmenting our understanding of their dynamic functions across diverse cellular contexts. By leveraging the complementary strengths of LC-MS and AP-MALDI imaging, this research enhances our understanding of spatial metabolite distribution in glioblastoma cells isolated from the invasive margin and suggests potential applications in patient tissue, offering a deeper exploration of disease mechanisms.

Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India

Global warming has led to an increase in extreme rainfall events, which often result in landslides, posing significant threats to infrastructure and human life. This study evaluated the effectiveness of the Capillary Barrier System (CBS) in enhancing slope stability along a vulnerable section of India’s National Highway 10 (NH10) during maximum daily rainfall. The GEOtop model was employed to conduct water balance simulations and obtain the pore–water pressure (PWP), which was then used to calculate the Factor of Safety (FoS). Results showed that CBS effectively delayed the rise in PWP, leading to lower peak values and smaller areas of very high and high risk levels. Spatial distribution mapping further confirmed that CBS minimized very high risk zones. At three historical landslide points, CBS slopes generally maintained FoS values above 1, demonstrating enhanced stability and improved resilience to extreme rainfall. These findings highlight the potential of CBS as a viable strategy for slope reinforcement in regions susceptible to heavy rainfall.

Spatial distribution, source apportionment, and health risks assessment of trace elements in pre- and post-monsoon soils in the coal-mining region of North Karanpura basin, India

Coal mining activities in the North Karanpura basin have significantly increased the trace element (TE) concentrations in the soil, resulting in soil pollution and potential health risks. To assess this, 113 soil samples, along with coal, shale, and overburden rocks, were collected from open-cast mining areas during pre-monsoon (Pre-M) and post-monsoon (Post-M) seasons. Seasonal analysis revealed higher TE concentrations in the Post-M period, especially in the SE direction, followed by NE and NW, likely due to surface runoff and deposition, demonstrating temporal variability in TE distribution which corroborated from the spatial distribution maps. Positive matrix factorization (PMF) model identified four factors: mixed sources (F1Pre-M: 37.6 %; F4Post-M: 28.9 %), coal-fired emissions (F2Pre-M: 20.5 %; F3Post-M: 26.0 %), overburden rocks (F3Pre-M: 25.5 %; F2Post-M: 16.7 %), and agricultural and lithogenic origin (F4Pre-M: 16.4 %) during the Pre-M period, attributed to coal mining. Post-M sources were similar, but agricultural and lithogenic origins were replaced by atmospheric deposition (F1Post-M: 28.4 %), enhanced by monsoon effects. Carcinogenic risk assessment revealed that As, Cr, and Ni exceeded acceptable levels for children via ingestion, though adults remained within safe limits. Inhalation and dermal contact were also considered, but ingestion posed the highest risk. The hazard index (HI) via ingestion showed that children had an HI of 1.6 in Pre-M, increasing to 2.66 in Post-M, highlighting their potential vulnerability to non-carcinogenic risks, while adults stayed within safe limits. The expansion of mining areas in the study region led to decrease in vegetative areas which could affect agriculture and local communities, raising a comprehensive environmental and public health issues. These results underline the need for implementing effective biannual soil monitoring and mitigation strategies, such as phytoremediation, bioremediation, rock dust remediation, chemical amendments and improved waste management, to reduce TE contamination.

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Early Season Crop Acreage Estimation for Pigeon Pea using SAR Data: A Case Study of Kalaburagi District, Karnataka

Crop acreage estimates using space-based observations during mid-season and pre-harvest periods are operationally provided for major Kharif and Rabi crops in India. These are critical inputs for planning and decision-making concerning food security, storage, pricing, and procurement. This work explores the potential of Satellite C-band Synthetic Aperture Radar (SAR) data in VV and VH polarization, acquired during monsoon season, for early crop acreage assessment of Pigeon Pea. The present study evaluates two classification approaches based on multi-temporal SAR data to generate a spatial distribution map of the pigeon pea crop in the Kalaburagi region of Karnataka. The temporal profiles of sigma nought (backscatter coefficient) and Entropy vs. Alpha Angle were studied with reference to crop phenology. Random forest algorithm has been used to classify multi-temporal SAR Ground Range Detected (GRD) data from June to November 2019. Temporal SAR data from June to early November with VV+VH polarization was optimal for tur area assessment with 85% accuracy. This study also examines phase and intensity information from 3-date SAR Single Look Complex (SLC) data with an unsupervised Wishart Classification approach for improved crop acreage estimation. The results emphasize the effectiveness of dual-polarimetric SAR data for timely crop inventory of Pigeon Pea during the Kharif season.

Trends in Atmospheric Nitrogen Deposition in Macedonia Studied by Using the Moss Biomonitoring Technique

This study examined the nitrogen content in moss samples collected across Macedonia over a 15-year period (2005, 2010, 2015 and 2020) from 72 consistent sampling locations. The nitrogen content was determined using the Kjeldahl method, providing insight into the trends of atmospheric nitrogen deposition across different regions. Descriptive statistical analyses, including spatial distribution maps, were used to compare the temporal variations and regional nitrogen levels. In addition, box-plots (P25–P75) and whiskers (P5–P95) were constructed to provide a comprehensive view of the variability across different tectonic units and zones, allowing for an in-depth understanding of the spatial distribution of nitrogen across the country. The study revealed that the median nitrogen content in moss samples decreased from 1.21% in 2005 to 1.04% in 2015, followed by a slight increase to 1.07% in 2020. The highest nitrogen concentrations were consistently found in areas with heavy agricultural activities and high traffic volumes, indicating the direct impact of these anthropogenic factors. The comparisons across regions and geological zones also highlighted the substantial variation in nitrogen levels, reflecting the diverse environmental pressures in different parts of Macedonia. This long-term analysis not only offers valuable insights into the trends in nitrogen pollution but also underlines the necessity for targeted policy interventions, particularly in the regions where nitrogen levels remain persistently high.

The evolution of spatial and temporal distribution of rainfall erosivity in Henan Province, central China

With the advancement of urbanization, there has been a significant reduction in cultivated land, accompanied by soil erosion. Concurrently, the regularity of rainfall in recent years has been erratic, adversely impacting the grain economy and agricultural development in certain regions. Henan Province, spanning the basins of the Yangtze River, the Yellow River, the Huaihe River, and the Haihe River, possesses complex hydrological conditions and serves as a pivotal agricultural zone in China. Therefore, this paper utilizes daily rainfall data collected over 54 years (1969–2022) from 112 rain measuring stations in Henan Province to calculate the rainfall erosivity using the Zhang model and the erosivity model from the first national water survey. Meanwhile, spatial analysis was performed using the Kriging interpolation method in the ArcGIS Geostatistical Wizard, resulting in detailed spatial distribution maps of rainfall and rainfall erosivity. The study also employed Wavelet and Mann–Kendall tests to analyze the abrupt changes, trends and periodicity of rainfall and rainfall erosivity within the target region. The findings indicate that the average rainfall (1969–2022) in Henan province was 718.26 mm, while the average rainfall erosivity (R) was 3213.46 MJ mm/(hm2 h). R values are positively correlated with rainfall intensity and volume, displaying an annual upward trend. Spatially, R values increase gradually from northwest to southeast, closely aligning with topographical variations. Additionally, the analysis revealed a predominant periodic cycle of 54 years in the precipitation patterns. These results offer valuable insights for environmental and agricultural management in other regions of central China.

Prevalence and association of musculoskeletal disorders with various risk factors among older Indian adults: Insights from a nationally representative survey.

Globally, Musculoskeletal disorders (MSDs) are the biggest contributor (17%) to years lived with disability. For offering diagnosis and appropriate health interventions options along with follow-up care, it is pertinent to have a thorough awareness of its associated risk factors. The study aims to assess the prevalence and association between MSDs and risk factors among the Indian older adults above 45 years. Prevalence of MSDs among 28,436 participants was estimated using nationally representative survey on older Indian adults. Spatial distribution maps were created to display the magnitude of MSD prevalence at Indian sub-national level. Association between presence of MSDs and associated risk factors (such as occupation, employment duration, physical activity, BMI, diabetes, hypertension, tobacco usage, and alcohol consumption) was explored through Multivariate logistic regression. P-value <0.05 was considered as statistically significant. MSD was prevalent in over half of participants (53.5 (52.9-54.1)%), with more among females and in those aged >60 years (60.4 (59.3-61.4)%). Overall, joint pain (41.9 (41.3-41.4)%) was more common than back pain (32.6 (32.0-33.1)%). Prevalence was highest in Manipur (81.1 (77.9-83.9)%) and lowest in West Bengal (33.1 (30.7-35.5)%). MSD presence was positively associated with certain occupational groups, pre-obesity (BMI 25-29.9 Kg/m2), currently hypertensive, vigorous physical activity among overall population. Tobacco usage was positively associated, while alcohol consumption was linked to a lower occurrence of MSDs for 45-60 years age group. Customised policy interventions can be developed for specific age category of older Indian adults and musculoskeletal health can be improved by addressing modifiable risk factors such as physical inactivity, tobacco usage, avoiding workplace risks in occupations requiring manual labour as revealed by this study.

Spatio-Temporal Monitoring and Assessment of Air Quality and its Impact on Public Health from Geospatial perspective over Haryana, India

Abstract. This study aims to evaluate the air quality of the Haryana state, India by utilizing Sentinel-5P satellite data. This study aims to track, quantify, and analyze the level of air pollutants in several regions of Haryana. By integrating and analyzing diverse satellite datasets, the study provides a comprehensive understanding of air quality conditions in the region and supports informed decision-making for pollution mitigation and environmental sustainability. This also examines the public health and environmental impacts of air pollution, identifies pollution sources, reviews existing policies, and also provides recommendations for prevention of air pollution. The outcomes of this study will improve the understanding of air quality dynamics in Haryana and provide preventive measures towards sustainable environmental practices. This paper presents a detailed assessment of air quality in Haryana, India, covering the period from 2019 to 2023 using satellite datasets. This evaluates the concentrations and spatial distributions of air pollutants across Haryana. The experimental results provide significant variations in pollution levels over the four years, with higher concentrations in 2019 and 2021, and lower levels in 2020 and 2023. Moreover, spatial distribution maps illustrate pollutant patterns, emphasizing the temporal and spatial changes in pollution levels. The results also highlight the ongoing challenge of increasing pollution in certain areas of Haryana and the need for continuous monitoring to improve air quality.

Multimode light generation in an injection semiconductor laser based on a chiral AlAs/(Al, Ga)As/GaAs microcavity

Experimental investigations of chiral injection AlAs/(Al, Ga)As/GaAs vertical-cavity surface-emitting lasers in the multimode generation regime are performed. A high circular polarization degree 70% of different generation modes measured with a high spectral resolution, is demonstrated. Detailed maps of spatial and angular distribution of laser radiation intensity were constructed.

Potential of Hyperspectral Data Combined With Optimal Band Combination Algorithm for Estimating Soil Organic Carbon Content in Lakeside Oasis

ABSTRACTAccurate estimation of soil organic carbon (SOC) content is essential for promoting regional sustainable agriculture and improving land quality. Visible and near‐infrared (Vis‐NIR) near‐Earth remote sensing spectroscopy has become an effective alternative to the traditional time‐consuming and costly methods due to its high‐resolution and nondestructive application, but it is vulnerable to the redundancy of spectral information and the overlap between bands. This study delves into the potential of optimal spectral parameters for estimating SOC in arid lakeside oases, using Bosten Lake in Xinjiang, China, as a focal point. Soil samples (0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm) were collected, and their SOC content and hyperspectral reflectance were measured. The spectral data underwent preprocessing techniques, including continuum removal (CR), standard normal variate (SNV), and continuous wavelet transform (CWT). SOC content was predicted using back propagation neural network models constructed based on one‐dimensional (1D), two‐dimensional (2D), and three‐dimensional (3D) correlation coefficients. Results showcased the effectiveness of the CWT method in accentuating potential spectral information and enhancing variable correlation. Among the indices, 3D exhibited the highest performance (R2 = 0.82, RPD = 2.02 for TDI‐1 at 0–10 cm; R2 = 0.85, RPD = 2.28 for TDI‐2 at 10–20 cm; R2 = 0.83, RPD = 2.24 for TDI‐1 at 20–30 cm; R2 = 0.86, RPD = 2.53 for TDI‐4 at 30–40 cm), followed by 2D and then 1D. These insights offer guidance for future strategies in hyperspectral data preprocessing and spectral index determination, facilitating SOC spatial distribution mapping and advancing sustainable agricultural planning. They also have implications for determining the spatial distribution of SOC content based on spatial interpolation, which would contribute to regional agricultural planning and sustainable development.

Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.

Using land use methodology to construct ring spatial variables for modeling and mapping spatial distribution of dust in snow cover

Using land use methodology to construct ring spatial variables for modeling and mapping spatial distribution of dust in snow cover

Spatial assessment of water quality in Mananga River in Talisay City, Cebu, Philippines

This paper focuses on the degradation of water quality in the Mananga River and aims to analyze spatial changes for the recommendation for rehabilitation. The use of ArcGIS and JMP software allows for the assessment of water quality and the identification of the causes of the contaminants coming from probable sources that are near the river. These activities are mainly rural areas and businesses that could have affected the degradation of the quality of the water in the river. By generating spatial distribution maps and conducting rapid profiling, this paper provides recommendations for rehabilitation. The significance of this paper lies in contributing to decision-making processes and the formulation of protective laws that can help in stop the degradation of the water quality of the river. Limitation includes the quarterly frequency of the water monitoring of the river. The results of this paper suggest that the Mananga River does not meet the requirements for DENR Administrative Order 2016-08 for type A rivers. The spatial distribution maps generated were able to visualize and identify the anthropogenic activities that may be affecting the water quality of the river. The DO showed a value from 0.0011 mg/L – 18.800 mg/L, BOD has a value ranging from 1.000 mg/L to 81.500 mg/L, 4.000 mg/L – 325.980 mg/L for TSS, and pH showed values from 7.400 to 8.800. These findings emphasize the need for government intervention and the development of automated river monitoring systems to facilitate testing and rehabilitation efforts.

Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China

Texture features have been consistently overlooked in digital soil mapping, especially in soil salinization mapping. This study aims to clarify how to leverage texture information for monitoring soil salinization through remote sensing techniques. We propose a novel method for estimating soil salinity content (SSC) that combines spectral and texture information from unmanned aerial vehicle (UAV) images. Reflectance, spectral index, and one-dimensional (OD) texture features were extracted from UAV images. Building on the one-dimensional texture features, we constructed two-dimensional (TD) and three-dimensional (THD) texture indices. The technique of Recursive Feature Elimination (RFE) was used for feature selection. Models for soil salinity estimation were built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), and Convolutional Neural Network (CNN). Spatial distribution maps of soil salinity were then generated for each model. The effectiveness of the proposed method is confirmed through the utilization of 240 surface soil samples gathered from an arid region in northwest China, specifically in Xinjiang, characterized by sparse vegetation. Among all texture indices, TDTeI1 has the highest correlation with SSC (|r| = 0.86). After adding multidimensional texture information, the R2 of the RF model increased from 0.76 to 0.90, with an improvement of 18%. Among the three models, the RF model outperforms PLSR and CNN. The RF model, which combines spectral and texture information (SOTT), achieves an R2 of 0.90, RMSE of 5.13 g kg−1, and RPD of 3.12. Texture information contributes 44.8% to the soil salinity prediction, with the contributions of TD and THD texture indices of 19.3% and 20.2%, respectively. This study confirms the great potential of introducing texture information for monitoring soil salinity in arid and semi-arid regions.

Mapping intertidal microphytobenthic biomass with very high-resolution remote sensing imagery in an estuarine system

Microphytobenthos (MPB) contributes significantly to estuarine primary production, so that quantifying its biomass is crucial for assessing their ecosystem functioning. Conventional sampling methods are labour-intensive, logistically challenging, and cannot provide a comprehensive spatial distribution map of MPB biomass. Satellite imagery has offered a feasible alternative for mapping large areas at various temporal and spatial resolutions. However, no imaging device with a spatial resolution consistent with the few square centimetres sampled in-situ has been used in the field. This makes it challenging to accurately relate field biomass measurements with remotely sensed radiometric observations. In this study, two similar multispectral sensors were mounted on an unmanned aerial vehicle (UAV) at different altitudes, as well as on a custom-built device specifically designed to acquire images at ∼1 m altitude, in order to collect very-high spatial resolution reflectance data of MPB biofilms at the Guadalquivir Estuary (Spain) mudflats. In addition, a hyperspectral spectroradiometer acquiring in-situ field reflectance was used for validation. Simultaneously, MPB samples were collected using a 2 mm depth contact corer method, which were analysed through high-performance liquid chromatography (HPLC) to measure the concentrations of major MPB pigments. To assess the relationship between the MPB pigments and different reflectance-based spectral indices, generalised linear mixed effects models (GLMMs) were used, achieving a significant positive relationship between chlorophylls and all spectral indices tested. These models were used to map microphytobenthic biomass, yielding a mean biomass in the range of 30–50 mg Chl-a m−2 in the Guadalquivir estuary during late winter. This study demonstrates the potential of low-altitude/high spatial resolution radiometric imaging as an efficient, rapid, and non-destructive addition to in-situ measurements of MPB biomass, providing exciting perspectives for the monitoring of estuarine systems on a millimetric scale of variability.

Monitoring water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

Monitoring water quality is crucial for water exchange, precise feeding, and quality control of water products in freshwater aquaculture. In light of the issue of spatial heterogeneity in freshwater aquaculture pond waters and the constraints of conventional sensor detection techniques and traditional machine learning models. In this study, UAV multispectral images were combined with four machine learning algorithms (Ridge, XGBoost, CatBoost, RF) and the Stacking model to model the estimation of Chlorophyll a (Chl-a) and Turbidity and map their spatial distribution. The findings indicate that, in contrast to machine learning models, the Stacking model of water quality parameter performs better with higher accuracy. Meanwhile,for Chl-a and Turbidity the optimal sub-model combination in the Stacking model varies, with the most effective estimation model for Chl-a concentration identified as RF-XGB-Ridge (R2 = 0.84, RMSE=1.882 µg/L, MAE=3.433 µg/L and Slope = 0.791). As to Turbidity, the RF-CAB-Ridge model demonstrates superior performance, with macro-averaged precision (macro-p) of 93.3 %, macro-averaged recall (macro-R) of 88.8 %, macro-averaged F1-score (macro-F1) of 0.895, and Kappa coefficient of 0.813. Furthermore, the results of the joint analyses, which included measured samples and management measures at the test site, demonstrated that the spatial distribution maps of Chl-a and Turbidity were in alignment with the current status of water quality at the test site. This consistency was observed across both temporal and spatial scales. The results demonstrate that the integration of UAV multispectral images with the Stacking model can enhance the precision of water quality parameter models, facilitates the examination of the spatial and temporal distribution of water quality parameters and the underlying influencing factors, and advances the capability for dynamic monitoring of water quality parameters in freshwater aquaculture regions. Concurrently, it offers fundamental theoretical and methodological assistance for the precise regulation of water quality in freshwater aquaculture ponds and the formulation of optimal production management strategies.

From unwanted to wanted: Blending functional weed traits into weed distribution maps

AbstractSite‐specific weed management (SSWM) is increasingly employed to reduce herbicide inputs. Incorporating functional traits of weed species allows for the selection of SSWM methods that effectively reduce the abundance of weeds with a high competitive potential (disservice) while preserving weeds that provide beneficial ecosystem services (service). In this study, we aim to assess relevant weed functional traits and translate this information into a spatial trait distribution map for weed (dis‐)service provision. The distribution of weed abundance in a field was recorded using a spatial grid. Data on functional traits for the recorded weed species were extracted from published datasets and combined into the two variables, service and disservice. Individual traits (service/disservice) were weighted for each pixel of the weed distribution map based on the number of individual plants per species. Principal component analysis was employed to generate independent variables to describe the potential for service and disservice provision. As a result, two (dis‐)service trait‐based distribution maps were generated: one highlights field areas that provide enhanced ecological services, while the other displays areas with a high disservice potential. The results show that around 61% of the area in the field had a high service potential. The area with a high disservice was slightly higher than the half of the area with a high service, while about 32% of the field has both high service and disservice potential in the same area. This study presents a spatially explicit approach to incorporate information on weed functional traits into SSWM approaches targeted at reducing weed competition while at the same time enhancing weed functional diversity.

Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.