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

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

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China.

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 µm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

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

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

Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique-a case study of Patan district Gujarat, India.

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

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

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

Ocean carbon emission prediction and management measures based on artificial intelligence remote sensing estimation in the context of carbon neutrality.

With rapid economic development, the gradual deterioration of the natural environment has posed unprecedented challenges to human social civilization. The marine economy, as an important part of economic development, is the breakthrough of economic transformation for many coastal countries. Additionally, green development and environmental impact assessment have become the focus of research in these countries. This study employs remote sensing technology, an efficient observational method, to significantly enhance the efficiency of ocean information observation. It investigates ocean carbon emissions within the framework of carbon neutrality. First, we identified the ships along the coastline based on marine remote sensing information through the YOLO (you only look once) framework. Second, we applied the LSTM (long short-term memory) method to combine the target identification results and the historical data of carbon emissions to complete the corresponding carbon emission data fitting. Finally, carbon emission data from the past three years in the offshore area of Dalian were used to make accurate predictions. The results suggested that the recognition rate of the proposed target detection method could reach 88%, and the LSTM method has shown the best performance in terms of absolute error for the subsequent short-term carbon emission prediction. This framework not only provides essential technical support for analyzing remote sensing information within the context of carbon neutrality but also introduces innovative insights for carbon emission prediction.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data.

An integrated, remotely sensed approach to assess land-use and land-cover change (LULCC) dynamics plays an important role in environmental monitoring, management, and policy development. In this study, we utilized the advantage of land-cover seasonality, canopy height, and spectral characteristics to develop a phenology-based classification model (PCM) for mapping the annual LULCC in our study areas. Monthly analysis of normalized difference vegetation index (NDVI) and near-infrared (NIR) values derived from SPOT images enabled the detection of temporal characteristics of each land type, serving as crucial indices for land type classification. The integration of normalized difference built-up index (NDBI) derived from Landsat images and airborne LiDAR canopy height into the PCM resulted in an overall performance of 0.85, slightly surpassing that of random forest analysis or principal component analysis. The development of PCM can reduce the time and effort required for manual classification and capture annual LULCC changes among five major land types: forests, built-up land, inland water, agriculture land, and grassland/shrubs. The gross change LULCC analysis for the Taoyuan Tableland demonstrated fluctuations in land types over the study period (2013 to 2022). A negative correlation (r = - 0.79) in area changes between grassland/shrubs and agricultural land and a positive correlation (r = 0.47) between irrigation ponds and agricultural land were found. Event-based LULCC analysis for Taipei City demonstrated a balance between urbanization and urban greening, with the number of urbanization events becoming comparable to urban greening events when the spatial extent of LULCC events exceeds 1000 m2. Besides, small-scale urban greening events are frequently discovered and distributed throughout the metropolitan area of Taipei City, emphasizing the localized nature of urban greening events.

Assessment of spatiotemporal variability of ultraviolet index (UVI) over Kerala, India, using satellite remote sensing (OMI/AURA) data.

The spatial and temporal dynamics of daily ultraviolet index (UVI) for a period of 18 years (2004-2022) over the Indian state of Kerala were statistically characterised in the study. The UVI measurements used for the study were derived from the ultraviolet-B (UVB) irradiance measured by the Ozone Monitoring Instrument (OMI) of the AURA satellite and classified into different severity levels for analysis. Basic statistics of daily, monthly and seasonal UVI as well as Mann-Kendall (MK) statistical trend characteristics and the rate of change of daily UVI using Theil-Sen's slope test were also evaluated. A higher variability of UVI characteristics was observed in the Kerala region, and more than 79% of the measurements fell into the categories of very high and extreme UVI values, which suggests the need of implementation of appropriate measures to reduce health risks. Although the UVI measured during the study period shows a slight decrease, most of the data show a seasonal variation with undulating low and peak values. Higher UVI are observed during the months of March, April and September. The region also has higher UVI during the southwest monsoon (SWM) and summer seasons. Although Kerala region as a single whole unit, UVI show a non-significant decreasing trend (-0.83), the MK test revealed the increasing and decreasing trends of UVI ranging from -1.96 to 0.41 facilitated the delineation of areas (domains) where UVI are increasing or decreasing. The domain of UVI increase occupies the central and southern (S) parts, and the domains of decrease cover the northern (N) and S parts of the Kerala region. The rate of change of daily UVI in domain of increase and decrease shows an average rate of 0.34 × 10-5 day-1 and -2 × 10-5 day-1, respectively. The parameters (rainfall, air temperature, cloud optical depth (COD) and solar zenith angle (SZA)) that affect the strength of UV rays reaching the surface indicate that a cloud-free atmosphere or low thickness clouds prevails in the Kerala region. Overall, the study results indicate the need for regular monitoring of UVI in the study area and also suggest appropriate campaigns to disseminate information and precautions for prolonged UVI exposure to reduce the adverse health effects, since the study area has a high population density.

Spatiotemporal patterns and evolutionary trends of eco-environmental quality in arid regions of Northwest China.

The arid regions of northwest China suffer from water shortages, low land quality, and a fragile ecological environment, while social and economic development has increased the ecological and environmental load. The spatiotemporal pattern and evolutionary trend of ecological environmental quality were investigated by constructing a remote sensing-based ecological environmental index (EQI) evaluation model incorporating four indicators: drought index (DI), soil erosion index (SEI), greenness index (GI), and carbon exchange index (CEI). The study found that between 2001 and 2020, the DI, the SEI, and the CEI in the northwest arid region exhibited a downward trend with reduction rates of - 3e-05, -0.0006, and -0.0018, respectively. However, the GI demonstrated an upward trend, with a growth rate of 0.002. The average EQI in 2020 was 0.315, indicating a fair grade, with only 11.56% falling above the medium level. A general increasing trend was observed throughout the study period in EQI, with an incremental rate of 0.0002. Areas with future improvements in EQI accounted for 57.547% and were principally located in the eastern part of Inner Mongolia, Qinghai, and the northern and southern portions of Xinjiang. Notably, land use was significantly correlated with EQI (p < 0.01), with a hierarchy of effects that ran: forest land (0.678) > cultivated land (0.422) > grassland (0.382) > wasteland (0.138). The highly robust findings presented here offer innovative methods for ecological and environmental monitoring in the arid region of the northwest, with potential implications at an international scale.

Assessment of heavy metals at mangrove ecosystem, applying multiple approaches using in-situ and remote sensing techniques, Red Sea, Egypt.

Mangrove areas are considered the most retention zone for heavy metal pollution as it work as an edge that aggregates land and sea sediments. This study aims to examine if the heavy metals' existence in the mangrove sediment is related to contamination or natural resources. In addition, it gives an interpretation of the origin of these metals along the Egyptian Red Sea coast. Twenty-two samples of mangrove sediments were collected and then, analyzed for metals (Mn, Ni, Cu, Fe, Cd, Ag, and Pb) using inductively coupled plasma mass spectroscopy (ICP-MS). Integration between the in-situ data, contamination indices, and remote sensing and geographical information science (GIS), and multivariate statistical analysis techniques (PCA) were analyzed to assess and clarify the spatial origin of heavy metals in sediment at a regional scale. The average concentration of heavy metals from mangrove sediments were shown to be substantially lower than the referenced value, ranging from moderate to significant except the levels of Ag were very high. The heavy metals concentrations were expected to be naturally origin rather than anthropogenic and that be confirmed by mapping of Red Sea alteration zones spots. These alteration zones are parallel to mangrove sites and rich by several mineralization types including heavy metals that are carried by flooding to the coastline. Remote sensing and GIS techniques successfully contributed to interpreting the pattern of the origin of heavy metals and discharging systems that control the heavy metals concentration along the Red Sea coast.

Utilization of the remote monitoring of cardiac implantable electronic devices in a diverse demographic cohort: Insights from a single-center observation.

BACKGROUND: Despite its clinical benefits, patient compliance to remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) varies and remains under-studied in diverse populations. OBJECTIVE: We sought to evaluate RM compliance, clinical outcomes, and identify demographic and socioeconomic factors affecting RM in a diverse urban population in New York. METHODS: This retrospective cohort study included patients enrolled in CIED RM at Montefiore Medical Center between December 2017 and May 2022. RM compliance was defined as the percentage of days compliant to RM transmission divided by the total prescribed days of RM. Patients were censored when they were lost to follow-up or at the time of death. The cohorts were categorized into low (≤30%), intermediate (31-69%), and high (≥70%) RM compliance groups. Statistical analyses were conducted accordingly. RESULTS: Among 853 patients, median RM compliance was 55%. Age inversely affected compliance (p < .001), and high compliance was associated with guideline-directed medical therapy (GDMT) usage and implantable cardioverter defibrillator (ICD)/cardiac resynchronization defibrillator (CRTD) devices. The low-compliance group had a higher mortality rate and fewer regular clinic visits (p < .001) than high-compliance group. Socioeconomic factors did not significantly impact compliance, while Asians showed higher compliance compared with Whites (OR 3.67; 95% CI 1.08-12.43; p = .04). Technical issues were the main reason for non-compliance. CONCLUSION: We observed suboptimal compliance to RM, which occurred most frequently in older patients. Clinic visit compliance, optimal medical therapy, and lower mortality were associated with higher compliance, whereas insufficient understanding of RM usage was the chief barrier to compliance.

Remote sensing-based drought hazard monitoring and assessment in a coastal plain: A principal component approach.

Accurate drought information is essential for preventing agricultural and societal losses. The indicators of how severe a drought is the deficiency in precipitation, soil moisture, and vegetation stress. The indicators were evaluated using the Precipitation Condition Index (PCI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI).The indices were combined using Principal Component Analysis to create the Synthetic Drought Index (SDI) for the evaluation of drought severity. The indices were estimated using multi-source remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) and Operational Land Imager (OLI) of various years. Temporal analysis showed that the district is drought-prone and deficiency of 65% of precipitation in northeast monsoon of 2016 and below average non-monsoon rainfall in 2017, caused drought and affected 223.5 Km2 in 2017. Below average precipitation in northeast monsoon of 2018 and below average non-monsoon rainfall in 2019, caused drought and affected 423 Km2 in 2019. The northeast coastal regions of Ottapidaram, Thoothukudi, and Vilathikulam taluks of the district were more severely prone to drought. Failure of monsoon is the root cause of water deficit in water bodies. The semi-arid coastal climate accelerates the evaporation of water in water bodies and causes soil moisture deficit that leads to drought in the coastal district. A sequential evaluation of this index can be used to identify the onset of drought and mitigate the effect of drought.

Spatial assessment of soil erosion by water using RUSLE model, remote sensing and GIS: a case study of Mellegue Watershed, Algeria-Tunisia.

Soil erosion is an important global phenomenon that can cause many impacts, like morphometry and hydrology alteration, land degradation and landslides. Moreover, soil loss has a significant effect on agricultural production by removing the most valuable and productive top soil's profile, leading to a reduction in yields, which requires a high production budget. The detrimental impact of soil erosion has reached alarming levels due to the exacerbation of global warming and drought, particularly in the arid climates prevalent in Tunisia and Algeria and other regions of North Africa. The influence of these environmental factors has been especially evident in the catchment of Mellegue, where profound vegetation loss and drastic changes in land use and cover, including the expansion of urban areas and altered agricultural practices, have played a significant role in accelerating water-induced soil loss between 2002 and 2018. The ramifications of these developments on the fragile ecosystems of the region cannot be overlooked. Accordingly, this study aimed to compare soil losses between 2002 and 2018 in the catchment of Mellegue, which is a large cross-border basin commonly shared by Tunisian-Algerian countries. The assessment and mapping of soil erosion risk were carried out by employing the Revised Universal Soil Loss Equation (RUSLE). This widely recognised equation provided valuable insights into the potential for erosion. Additionally, changes in land use and land cover during the same period were thoroughly analysed to identify any factors that may have contributed to the observed risk. By integrating these various elements, a comprehensive understanding of soil erosion dynamics was achieved, facilitating informed decision-making for effective land management and conservation efforts. It requires diverse factors that are integrated into the erosion process, such as topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results show that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximately 14,000 new areas vulnerable to erosion by water in 2018 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2018, leading to an increase in total estimated soil loss of 54,000 t/ha in 2018 compared to around 25,500 t/ha in 2002. Maps of erosion risk show that highly eroded areas are more frequent downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plans and for land use preservation.

Earth observation satellite data-based assessment of wetland dynamics in the Kashmir Himalaya.

Earth observation (EO) technology offers enormous opportunities to assess the magnitude and patterns of spatial variability in wetlands over time. This study aims to assess the spatial and temporal changes in the wetlands of the Kashmir valley using multiple remote sensing satellite data products, Geographic Information System (GIS), and field observations. Moreover, role of major factors operating at different time scales including regional geology, climate, and human activities in driving the wetland change is presented. The dynamics of the wetlands are illustrated in the occurrence, seasonality, and recurrence of surface water, land cover transitions and loss patterns particularly for the period from 1984 to 2021. Constituting about 3% (495 Km2) of the total area, substantial and variable patterns of seasonal and annual changes are exhibited by the wetlands. The main transitions of the water surface reveal that 2% of the area has changed from permanent to seasonal; 8% is lost; 15% is new seasonal; 0.12% is permanently lost; and 0.3% is new permanent. About 22% of the area reveals increase in the intensity of water surface occurrence, whereas 44% shows no change, and 34% exhibits decrease. Bathymetric analysis suggests that the average depth of the wetlands ranges between 0.6 and 16.6 m. In general, alpine wetlands are relatively deeper and mostly static in their structure whereas those in the floodplain are shallow, fragmented, and showing signs of depletion during the assessment period. The results of this assessment will inform the policy on conservation and sustainability of wetlands in the Kashmir Himalaya.

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach.

Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers' basins is nearly identical. The Kunhar River is one of KP's dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions. Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin's northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study's results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.

Cost-effectiveness of remote patient monitoring for First Nations peoples living with diabetes in regional Australia.

The aim of this study was to determine the cost-effectiveness of remote patient monitoring (RPM) with First Nations peoples living with diabetes. This study was set at the Goondir Health Service (GHS), an Aboriginal and Torres Strait Islander Community-Controlled Health in South-West Queensland. Electronic medical records and RPM data were provided by the GHS. Clinical effectiveness was determined by comparing mean HbA1c before and after enrolment in the RPM service. Our analysis found no statistically significant effect between the mean HbA1c before and after enrolment, so this analysis focused on net-benefit and return on investment for costs from the perspective of the GHS. The 6-month RPM service for 84 clients cost AUD $67,841 to cover RPM equipment, ongoing technology costs, and a dedicated Virtual Care Manager, equating to $808 per client. There were 199 additional client-clinician interactions in the period after enrolment resulting in an additional $4797 revenue for the GHS. Therefore, the program cost the GHS $63,044 to deliver, representing a return on investment of around 7 cents for every dollar they spent. Whilst the diabetes RPM service was equally effective as usual care and resulted in increased interactions with clients, the cost for the service was substantially more than the additional revenue generated from increased interactions. This evidence highlights the need for alternative funding models for RPM services and demonstrates the need to focus future research on long-term clinical effects and the extra-clinical benefits resulting from services of this type.

Landscape ecological risk assessment of an ecological area in the Kubuqi desert based on Landsat remote sensing data.

Conducting ecological risk assessment of fragile ecological landscapes is a prerequisite for building an ecological security pattern and a necessary consideration for sustainable development. Engebei ecological demonstration zone is a typical ecologically fragile area located in the Kubuqi Desert. To explore the ecological status of Engebei, an ecological risk assessment model is used to assess its ecological risk, and the spatial correlation analysis is conducted based on the Moran index. The optimal grain size is obtained through grain size effect analysis, which is the foundation of landscape pattern analysis. The landscape ecological risk assessment model is constructed by the landscape indexes. Based on the division of small ecological risk zones, a spatial correlation analysis of ecological risks is conducted on Engebei. Results manifest that: (1) Overall, from 2005 to 2021, its spatial distribution features of landscape ecological risk level are relatively-high and high in the middle, and gradually reduce in the north-south direction, as shown below: the relatively-low ecological risk areas are widely spread, and the overall risk index decrease from 0.1944 to 0.1940; the area of low and high-level ecological risk areas show a decreasing trend, which decrease by 5.0102 km2 and 1.3132 km2 respectively; the area of relatively-low, middle, and relatively-high-level ecological risk areas increase by 0.2655 km2, 3.7803 km2, and 2.4852 km2, respectively. (2) The ecological risk value is correlated positively with spatial distribution, and the spatial aggregation forms are primarily low-low and high-high. (3) The ecological risk values in Engebei have a significant spatial correlation, and the spatial distribution shows a clustering effect, which is consistent with the spatial distribution. The study has certain reference value for the development and comprehensive regulation of ecological construction in Engebei, even in other ecologically fragile areas.

Assessment of Forest Ecosystem Variations in the Lancang-Mekong Region by Remote Sensing from 2010 to 2020.

Five countries in the Lancang-Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang-Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang-Mekong region, which is crucial for implementing effective conservation strategies.

Quantitative assessment of soil condition, basic environmental factors and productivity of Linum usitatissimum in the steppe zone of Kazakhstan using the remote sensing method.

The influence of environmental factors, such as lack of water and uneven rainfall, depletion of nutrients in the soil and reduced soil fertility, planting patterns and plant density, uneven growth stages, are the main limiting factors that hinder the growth of agricultural production in arid regions. The aim of the study was to assess the potential of Sentinel-2 to quantify soil conditions, which can improve the understanding of spatiotemporal dynamics in organic agriculture in the steppe zone of Kazakhstan and improve productivity management of Linum usitatissimum. In the course of the research, the influence of individual factors of the general environmental impact, such as the influence of humidity, meteorological conditions, the content of individual nutrient components of the soil on the yield, was studied. The meteorological conditions in this region in 2021 and the data of agrochemical analysis of the soil on which the oilseed crop was grown were evaluated. Sentinel-2 satellite images were used to determine the NDVI and GNDVI indices. A high content of nitrate nitrogen (12.3-16.2 mg/kg), a very low level of available phosphorus (3-10 mg/kg), and a high content of potassium (289-420 mg/kg) were found in the soil. A low content of humus (2.68-3.31%) and sulfur (1.1-4.9 mg/kg) was found. A study of the NDVI growth index showed that the highest value was reached by the period of July 20, 2021. After this period, a decrease in the vegetation index was observed. In conditions of severe drought, this change occurred earlier than under favorable conditions, and correlated with low flax yield (1.6-6.9 c/ha). This study demonstrates the potential of Sentinel-2 for quantifying soil conditions, which not only improves our understanding of spatial-temporal dynamics and environmental components in organic agriculture in the steppe zone of Kazakhstan, but also improves the management of Linum usitatissimum productivity.