Coastal areas are dynamic, shaped by natural processes and human activities, making accurate prediction of shoreline and land use changes crucial for sustainable management. This study presents a hybrid modeling framework that combines CA-Markov and machine learning to enhance land use/land cover (LULC) and shoreline change projections in Oman's vulnerable coastal regions. Coastlines were delineated using multi-temporal Landsat images (1997-2006-2015-2024) and the Normalized Difference Water Index, while erosion and accretion rates were quantified using End Point Rate and Linear Regression Rate analyses. Results from 1997 to 2024 show substantial spatial variability, with urban localities such as Rakhyut experiencing significant erosion (-1.81m/year) and areas like Bawshar showing accretion (1.41m/year). Coastal LULC changes reveal rapid urban expansion, as seen in Muscat's built-up area, which increased from 10.31km² in 1997 to 116.41km² in 2015. Four models-CA-Markov, CA-Markov + XGBoost, CA-Markov + CART, and CA-Markov + RF-were evaluated for future LULC prediction. The hybrid CA-Markov + RF model achieved the highest predictive performance, increasing overall accuracy from 0.905 (CA-Markov) to 0.935 (CA-Markov + RF) on the test dataset, highlighting the capability of machine learning models. Projections for 2033 indicate continued urban growth, particularly in Salalah and Sohar, alongside reductions in vegetation in arid regions.

Not always in sync: Environmental drivers of the relationship between tree radial growth and vegetation indices in Central Europe.

Farm dams are significant methane (CH4) sources in agricultural landscapes. Fencing them to limit livestock access reduces organic matter and nutrient inputs, thereby limiting CH4 emissions. However, existing studies on the benefits of fencing are constrained by short durations, omission of ebullitive fluxes, limited spatial and temporal coverage, and small sample sizes. Here, we report a large-scale, multi-season assessment of total CH4 (diffusive + ebullitive) and carbon dioxide (CO2) fluxes from fenced and unfenced farm dams, along key environmental drivers. We monitored 113 farm dams in temperate mainland south-eastern Australia over 2 years, amounting to 39,552 and 45,408 hourly observations of total CH4 and CO2 fluxes, respectively. We integrated field-measured emissions with Sentinel-2 indices, topo-climate variables, and geostatistical models to identify flux drivers, quantify temperature sensitivity, and spatially extrapolate mitigation potential across Local Government Authorities (LGAs). We found that fencing reduced CH4 fluxes by 66%-82% across seasons while also significantly lowering the temperature sensitivity of CH4 fluxes, slowing the exponential rise in emissions under warming conditions. Specifically, CH4 fluxes in fenced dams increased by 71% per 10°C warming (Q10 = 1.71, EM = 0.4 eV), compared to unfenced dams increasing by 275% (Q10 = 3.75, EM = 0.98 eV). CH4 fluxes were driven by temperature, rainfall, and hydrological proxies (Modified Normalized Difference Water Index, MNDWI; Floating Algae Index, FAI), while CO2 fluxes responded to rainfall and Normalized Difference Water Index (NDWI). Extrapolating our findings across the study area (~526,296 km2), fencing all farm dams could cut CH4 fluxes by 1.16-1.35 kt year-1. By combining high-resolution emission data with scalable management strategies, this study offers a framework to improve greenhouse gas inventories and guide targeted climate mitigation in agriculture.

Area dynamics of alpine lakes in the Yamzhog Yumco Basin: Optimized water indices reveal spatiotemporal patterns and key drivers

BACKGROUND General anaesthesia during pregnancy requires close monitoring of both mother and foetus and therefore represents a special challenge. Little is known about its impact or its correct management for pregnant women and their foetuses. Therefore, foetoscopic spina bifida repair employing general materno-foetal anaesthesia offers a great opportunity to investigate and further characterise its effects on mother and foetus that would otherwise remain undetermined. OBJECTIVES To evaluate our standard protocol for the anaesthetic management of foetal spina bifida repair with respect to maternal and foetal haemodynamics. DESIGN A retrospective cohort study. SETTING Single-centre study, University Medical Centre Mannheim, University of Heidelberg. PATIENTS We analysed 70 patients in this study. All interventions for foetal spina bifida repair between June 2018 and December 2022 were eligible for this study. MAIN OUTCOME MEASURES During foetoscopic spina bifida surgery, we evaluated materno-foetal haemodynamics and characteristics and safety of our anaesthetic standard protocol. Blood gas analyses (BGAs) and pulse contour cardiac output (PiCCO) analyses were performed regularly. Transabdominal sonography including Doppler analyses of the umbilical artery as a proxy for foetoplacental haemodynamics were performed at predefined time points throughout the procedure. RESULTS During the course of anaesthesia, a significant drop of both median [IQR] maternal base excess and pH were observed: -3.6 [-2.6 to -4.6] vs. -7.1 [-5.3 to -8.25] and 7.35 [7.33 to 7.37] vs. 7.32 [7.29 to 7.35], respectively. PiCCO analyses suggested an increasing need for fluid therapy resulting in a significant increase in the median [IQR] extravascular lung water index, 7 [7 to 8] vs. 8 [7 to 9]. The pulsatility index of the umbilical artery increased during the course of surgery. Despite their significance, these changes did not represent clinically meaningful differences. CONCLUSIONS General materno-foetal anaesthesia could be performed without meaningful effects on materno-foetal haemodynamics and maternal blood gases with the appropriate materno-foetal monitoring in a large number of foetoscopic surgical procedures. There is growing need to enable closer foetal monitoring with respect to depth of anaesthesia, vital signs and pain perception.

Majuli, the world's largest inhabited river island, is undergoing severe riverbank erosion and vegetation depletion, endangering its cultural heritage and the livelihoods of its 0.2 million residents. This study utilizes Landsat series satellite data from 1987, 1999, 2011 and 2023, integrated with Geographic Information System (GIS) software, to quantify changes in land area and vegetation cover over 36 years. Digitizing land area using the Normalised Difference Water Index (NDWI) method analysis indicates that erosion significantly exceeds deposition, with the island's area declining from 726.83 sq. km. (as per digitization) in 1987 to 650.17 sq. km. in 1999, briefly increasing to 717.84 sq. km. in 2011, before decreasing to 654.18 sq. km. in 2023. Vegetation analysis, utilizing the Normalized Difference Vegetation Index (NDVI) method, Land Use and Land Cover (LU/LC) classifications, reveals a drastic reduction in dense vegetation from 0.40 sq. km. in 1987 to none in 2023 and in sparse vegetation from 59.26 sq. km. to 2.28 sq. km. The study emphasizes that increasing population pressure, fluvial activities and agricultural demands are key factors exacerbating these environmental changes. It recommends integrated restoration and conservation efforts involving Government agencies, NGOs and local communities to mitigate erosion and to restore vegetation on Majuli.

Mosquito-borne diseases are those that are transmitted by the bite of an infected mosquito. Stagnant bodies of water are frequently preferred as mosquito breeding places. However, from producing eggs to the final stage, several elements contribute to its incubation, maturity, and growth to the point where it is capable of biting and transmitting diseases. The primary goal of this research is to focus on connected environmental determinants that provide optimal breeding locations and vulnerability mapping of mosquito-borne diseases using geospatial techniques and a decision-making approach. The analytical hierarchy process was combined with a geographic information system to create a map of mosquito-borne diseases in Muktsar district of Punjab state. The weights of selected variables were determined using a choice-based varied ranking method, which involved building a pair-wise comparison matrix. Initially, ten important environmental parameters were selected to determine their weight using a pair-wise comparison matrix. At the same time, the weight of each related element was employed as a geo-database to aid with overlay analysis. The consistency ratio was derived to evaluate the decision-making process and significance measurement. The consistency ratio of choice factors was found to be 0.0470, which is less than 0.1 and regarded consistent and acceptable. According to the study's findings, proximity to water bodies is a major influence, followed by moisture content, water index, availability of shade area, and the presence of vegetation in mosquito-borne disease prevalence. The current findings demonstrate the wide range of uses of satellites data and spatial techniques in epidemic diseases zonation. Keywords: Mosquito-borne diseases, Geospatial analysis, Analytic Hierarchy Process, Public health

This study assesses the seasonal variation and irrigation suitability of Karanja Reservoir water, Bidar, Karnataka (India) from January 2023 to December 2024. The surface and bottom water samples were collected seasonally and were evaluated for irrigation water indices such as Electrical Conductivity (EC), Total Hardness (TH), Sodium Adsorption Ratio (SAR), Soluble Sodium Percentage (SSP), Percentage Sodium (%Na), Residual Sodium Carbonate (RSC), Kelly's Ratio (KR), Magnesium Hazard (MH), Permeability Index (PI), Potential Salinity (PS), Irrigation Water Quality Index (IWQI), US Salinity Laboratory (USSL), Corrosivity Ratio (CR) and Chloro-Alkaline Indices (CAI1 and CAI2). The results revealed TH, CR, PS, %Na exceeded the permissible limit. A high CR indicated that water should not be transported using metal pipes. Based on the USSL classification, the reservoir water falls within the C2–S1 category, indicating medium salinity and low sodium hazards, while the IWQI classifies it as low-restriction water. Overall, the Karanja reservoir water is moderately suitable for irrigation, provided appropriate soil and water management practices are adopted. Measures such as periodic soil amendments and cultivating salt-tolerant or moderately salt-tolerant crops can help mitigate long-term sodicity and salinity impacts. This study emphasizes the need for ongoing monitoring and sustainable irrigation planning to maintain soil health and support long-term agricultural productivity.

This study investigates the spatiotemporal dynamics of urban expansion in major cities of Jilin Province using Landsat satellite imagery acquired in 2002, 2012, and 2022. To accurately extract urban land cover, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were employed, achieving an overall accuracy of 83% for urban boundary delineation. A 5 km grid-based analysis was further applied to quantify and map the spatial patterns of urban growth. Results indicate that the total urban land area in Jilin Province expanded from 1,100 km² in 2002 to 2,430 km² in 2022, with major cities (e.g., Changchun) experiencing the most substantial growth. Spatially, high urban coverage was concentrated in central urban agglomerations and gradually expanded outward, with the period 2012–2022 witnessing the most intense growth in core city regions. Topographic conditions, industrial development, regional policies, and transportation infrastructure were identified as the key drivers of these urban expansion patterns. This research provides critical insights into the dynamics of urbanization and land use change in Jilin Province, supporting evidence-based urban planning and land resource management.

ABSTRACT Evaporite mapping in hyperarid regions is crucial for understanding surface hydrology, salt crust dynamics, and environmental change. Terrain classification in these settings is hindered by spectral overlap among surface types and limited remote sensing coverage. To our knowledge, no previous workflow has combined Sentinel-1 radar data with Sentinel-2 indices for mapping evaporites in a Mars-analog environment. We developed a lightweight, rule-based fusion of horizontal transmit – horizontal receive (HH)-polarized Sentinel-1 backscatter with the Normalized Difference Water Index (NDWI) and Normalized Difference Snow Index (NDSI) from Sentinel-2, optimized for simplicity and low computational demand. Applied to a salt flat in northern Chile, moist salts accounted for ~67% and dry evaporites for ~5% of the area. The fused method achieved an overall agreement of more than 80% and F1-scores above 0.75, improving accuracy by 10–15% compared to spectral-only approaches. This sensor-independent framework supports efficient mapping of evaporitic surfaces in data-limited environments. It is directly transferable to planetary surface analysis, including analog studies and autonomous terrain triage during mission operations.

ABSTRACT Riparian ecosystems in Mediterranean landscapes are critical for biodiversity conservation and ecosystem services. This study investigates riparian vegetation change dynamics in a Mediterranean ecosystem in southern Italy, focusing on the potential relationship with environmental and anthropogenic factors contributing to the observed dynamics over the study period. We used 25-year of Landsat imagery to assess riparian vegetation cover and surface water changes, analyzing temporal trends of Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI), respectively. Statistical trend analysis revealed for the 96% of the changing areas a significant increase in riparian vegetation cover classified between “light” to “moderate improvements” in terms of trend’s magnitude. The greening process is particularly pronounced in areas with gentler slopes and higher Strahler stream orders. Conversely, we found a decrease in the surface area covered by water over the observed period. We used Structural Equation Model (SEM) to identify the relationship between water availability (surface water, precipitation, soil moisture) and riparian vegetation change trends. Reduction in surface water appears as the primary factor influencing vegetation change. Although the contribution to vegetation dynamics of topography, environmental conditions, land use and reduced anthropogenic pressure e.g. decline in traditional agriculture and land abandonment remain negligible outside the core of conceptualized model, their role in creating favourable conditions for riparian vegetation expansion cannot be overlooked. An understanding of these patterns and interactions is important for the development of effective adaptive management strategies to sustain both ecosystem functions and conservation efforts in riparian areas.

Abstract. This study employs a robust remote sensing framework to analyze the spatio-temporal dynamics of cage aquaculture and its effects on water quality in Yangcheng Lake, China. Utilizing time-series Landsat 8 data (2013–2024), we accurately mapped the aquaculture extent by combining spectral water indices for water body delineation with spectral vegetation indices and Gray-Level Co-occurrence Matrix (GLCM)-based texture analysis. This approach achieved high mapping accuracies, with overall accuracies ranging from 95.96% to 98.74% and Kappa coefficients between 0.84 and 0.94, demonstrating the effectiveness of integrating spectral and textural information. Spatio-temporal analysis revealed a significant 40% reduction in aquaculture extent, from 30.41 km2 in 2013 to 17.86 km2 in 2024, directly linked to the Yangcheng Lake Action Plans. Water quality analysis using high-resolution monthly total nitrogen (TN) and total phosphorus (TP) data showed improvement from predominantly Grade IV to Grade III status, with the mean TP concentration exhibited a decrease by approximately 6.2% (from 0.0660 mg/L in 2013 to 0.0619 mg/L in 2023). Notably, a distinct lag between aquaculture reduction and water quality improvements was observed, highlighting the need for a sustained ecosystem recovery period. This research highlights the vital role of dynamic GIS for environmental monitoring and policy evaluation for fostering sustainable aquaculture and protecting lake ecosystems.

This study investigates the dynamics of environmental transformation in the southeastern basins of Madre de Dios, Peru, by integrating multi-spectral remote sensing data with advanced machine learning methodologies. To capture and quantify land surface changes over time, satellite imagery from Landsat and Sentinel missions was utilized to derive key spectral indices—specifically, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). These indices provided critical insights into vegetation health and surface water distribution. To manage the high dimensionality of the spectral data, Principal Component Analysis (PCA) was applied, enabling more efficient data interpretation and visualization. Subsequently, unsupervised K-means clustering was employed to classify land cover types and detect spatial patterns of change without prior labeling. The analysis revealed a significant decline in dense vegetative cover, accompanied by a notable expansion of bare soil and surface water areas. These findings point to accelerating environmental degradation in the region, likely driven by both natural and anthropogenic pressures. The methodological framework adopted in this study demonstrates strong potential for scalable, data-driven environmental monitoring and offers a replicable model for assessing land cover dynamics in other ecologically sensitive regions.

This study analyzed of spatiotemporal change characteristics of Poyang Lake from 1984 to 2021 by the dataset of Landsat series of satellite imagery and JRC Global Surface Water based on the Google Earth Engine platform. The normalized difference water index combined with the Otsu method was used to extract the water area. The results indicated that from 1984 to 2021, the interannual variation of Poyang Lake’s water area presented the characteristics of “fluctuation decline—fluctuation rise—overall decline—overall increase”. Additionally, the lake areas in Yongxiu, Xinjian, Nanchang, and Poyang were the primary regions contributing to Poyang Lake’s overall area changes. The seasonal variation of Poyang Lake is obvious in a year, the area in summer was larger than that in winter. Compared with 1984, 0.03% of the water area of Poyang Lake in 2021 disappeared permanently, and 8.45% of the water area changed from permanent to seasonal. Lake area changes were jointly driven by climate change and human activities. The average annual temperature increases, agricultural irrigation, reclamation of surrounding lakes and water conservancy engineering caused the reduction in lake area. Increased annual precipitation and the implementation of environmental protection policies were the main factors for the increases in lake area.

Agroforestry plays a pivotal role in mitigating climate change and supporting rural livelihoods. Especially in coastal regions, it aids soil conservation, provides biophysical protection, and diversifies income sources. Through conventional approaches, mapping the arecanut-based agroforests is difficult. This study utilised machine learning models to identify arecanut based traditional agroforestry systems using Sentinel-2 satellite data in Goa, India. A total of 374 non-agroforestry and 70 agroforestry locations were collected for model training and testing. Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) were employed. The Boruta algorithm was applied for feature selection and to improve the model accuracy. The findings showed that the GBM model had significantly higher overall accuracy (0.86) and kappa (0.83) scores than other models during validation. The Boruta analysis revealed NDWI2, B3, and SLAVI as important variables indicating the importance of water indices and vegetation parameters in mapping agroforestry. The GBM model achieved high accuracy in identifying arecanut based agroforestry region amounting to 58.64 km2 followed by the RF with 53.36 km2 and the SVM with 23.21 km2. Using the average of three models, the estimated area under arecanut based agroforestry in Goa was 45.1 km2. The area of applicability (AOA) analysis based on GBM revealed that 97.32% of the total geographic area of Goa was inside AOA indicating better distribution of collected ground truth data. This paper demonstrated efficiency of machine learning algorithms for accurate mapping of agroforestry systems to support land use planning, resource conservation and management in coastal environments. Subsequent studies utilizing hyperspectral sensors can improve the efficiency of machine learning-based agroforestry mapping methods.

Multi-decadal village-scale assessment of riverbank erosion-accretion and restoration priorities in the Lower Ganga.

With the intensifying trend of population aging, the positive effects of Urban Green Space (UGS) on Psychological Well-being (PW) among older adults have garnered increasing attention. Previous studies examined the unidirectional pathways through which objective UGS exposure indicators influenced PW via Sense of Place (SOP) or Perceived Restorativeness (PR). However, little empirical work has addressed how UGS exposure affects PW in older adults through a dual mediation pathway encompassing both SOP and PR. To address this gap, this study investigated representative urban parks in Fuzhou, China. Using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) as objective exposure indicators, along with survey data capturing older adults’ perceived characteristics (PC) of UGS, SOP, and PR, we developed a multilevel structural equation model to explore how NDVI, NDWI, and PC influence PW through the dual mediators of SOP and PR. Results indicated that (1) NDVI and PC influence PW either directly or indirectly via the mediators SOP and PR, with PC exhibiting a significantly stronger effect than NDVI; (2) NDWI has no significant effect on SOP and PW, but it indirectly influences PW through PR; (3) PR moderated the link between SOP and PW; (4) under the dual-pathway mechanism, PR contributes more substantially to PW than SOP. This study enriches the understanding of how UGS contributes to PW and advocates for integrating exposure characteristics, place qualities, and restorative elements into the framework of age-friendly city planning to guide targeted health interventions for the elderly. Moreover, SOP–PR insights from an East Asian city inform global aging urbanism.

Abstract Climate over China is mainly governed by monsoons, which bring frequent and intense convective precipitations. Adequate knowledge of monsoon characteristics is necessary for understanding the development of extreme weather events. Given that the monsoon is usually associated with distinct variations in water vapor between dry and wet seasons and rapid dry‐wet transitions, understanding the distribution and variability of water vapor can help characterize the monsoon. In this work, global navigation satellite system (GNSS) data from over 1,000 stations densely distributed in China were used to obtain zenith tropospheric delays (ZTDs). Combined with ERA5 meteorological data, precipitable water vapor (PWV) grid products were retrieved during the period 2016–2020 to characterize the Asian Summer Monsoon (ASM). To address the issue of data gaps in GNSS‐derived PWV, we employed a random forest machine learning method using ERA5‐derived PWV for data imputation ensuring the integrity of the products. We first utilized the bimodality of PWV statistical distribution and selected a suitable metric to quantify the monsoon impact across different regions. Subsequently, the normalized precipitable water indexes (NPWI) were constructed based on temporal variations in PWV to describe the monsoon movement characteristics. The average monsoon onset in China occurs from late May to mid‐June (Day of year (DOY) 150–170), whereas the monsoon retreat takes place in September (DOY 252–274). The interannual variability of the monsoon onset and retreat times is similar with a variation of around 10 days. Generally, the monsoon advances from southeast to northwest over China with an uneven speed.

Ensuring the purity of water sources is a paramount global challenge, necessitating the development of highly sensitive and rapid detection technologies. In this work, a novel Zeonex-based photonic crystal fiber (PCF) sensor is designed and numerically analyzed for the effective differentiation of pure and polluted water by identifying their unique fingerprints in the terahertz (THz) spectrum. The proposed structure features a rectangular core for analyte infiltration, surrounded by a unique hybrid cladding, meticulously engineered with four inner “mode-shaping” rectangular air holes and an outer “confinement” ring of elliptical air holes. This complex topology is strategically designed to maximize the core-power fraction while ensuring robust mode confinement, enabling the exceptional performance metrics observed. The guiding properties and sensing performance of the sensor are rigorously scrutinized using the Finite Element Method (FEM) over a broad frequency range of 0.5 to 3 THz, accommodating analytes with refractive indices from 1.33 to 1.46. This range is specifically chosen to cover the refractive index of pure water (≈1.33) and a broad spectrum of common chemical and biological pollutants. The simulation results demonstrate the exceptional performance of the sensor. For polluted water, the sensor achieves an ultra-high relative sensitivity of 99.6% with a negligible confinement loss of 1.4 × 10−11 dB/m at an operating frequency of 3 THz. In contrast, pure water exhibits a high sensitivity of 96% and a confinement loss 9.4 × 10−6 of dB/m at the same frequency, showcasing a remarkable capability to distinguish between different water qualities. The superior sensitivity, extremely low loss, and structurally feasible design make the proposed PCF sensor an up-and-coming candidate for real-time water quality monitoring within the THz domain.

Abstract Remote sensing monitoring of small-lake eutrophication faces challenges such as sparse data, insufficient synergy of multi-source data, and limited model generalization performance. Hence, this study developed a scenario-aware modeling framework for the trophic level index (TLI) by integrating multi-source imagery data from Sentinel-2, GF-1, HJ-2, and PlanetScope, using Dongqian Lake in Zhejiang Province, China as the case study. The cross-sensor prediction accuracy was evaluated using algorithms such as CatBoost Regression (CBR), XGBoost Regression (XGBR), TabPFN Regression (TPFNR), and Linear Regression (LR). Meanwhile, the influence of input features was quantified by SHapley Additive exPlanations (SHAP). The main results found that : (1) Overall annual mean values of total nitrogen/total phosphorus ratio (TN/TP) and TLI were 22.13 and 37.36 ± 4.99, respectively, indicating a mesotrophic and phosphorus-limited state in Dongqian Lake. (2) TLI exhibited the strongest correlation with water color and algal spectral indices, including Normalized Difference Water Index (NDWI), Normalized Green–Red Difference Index (NGRDI), and Blue–Green Ratio (BGR). (3) CBR demonstrated the strongest cross-sensor generalization capability across different imagery, with only minor variations in prediction accuracy (ΔR ≈ 0.07–0.15). Feature attribution analysis identified NDWI, NGRDI, and BGR as primary contributing features for the CBR model. (4) Integrating high-frequency multi-source remote sensing imagery with 27 field surveys achieved seamless monitoring of the TLI. The spatial distribution of TLI showed distinct seasonal variations, with higher values observed in nearshore areas and lower values in the lake center. TLI values were relatively low in spring, but surged sharply and remained elevated in summer. This study provided a reference basis for detailed remote sensing monitoring and management of eutrophication in small lakes.