Land subsidence in Bangkok, a pressing environmental challenge, demands sustained long-term policy interventions. Although mitigation measures have successfully alleviated subsidence rates within inner Bangkok, neighboring provinces continue to experience escalating rates. Conventional land-based monitoring methods exhibit limitations in coverage, and the anticipated nonlinear contributions of climatic and socioeconomic factors further complicate the spatiotemporal distribution of subsidence. This study aims to provide future subsidence predictions for the near (2023-2048), mid (2049-2074), and far-future (2075-2100), employing Interferometric Synthetic Aperture Radar (InSAR), Random Forest machine learning algorithm, and combined Shared Socioeconomic Pathways-Representative Concentration Pathways (SSP-RCPs) scenarios to address these challenges. The mean Line-of-Sight (LOS) velocity was found to be -7.0 mm/year, with a maximum of -53.5 mm/year recorded in Ayutthaya. The proposed model demonstrated good performance, yielding an R2 value of 0.84 and exhibiting no signs of overfitting. Across all scenarios, subsidence rates tend to increase by more than -9.0 mm/year in the near-future. However, for the mid and far-future, scenarios illustrate varying trends. The \'only-urban-LU change\' scenario predicts a gradual recovery, while other change scenarios exhibit different tendencies.

Based on ambient air quality data, meteorological observation data, and satellite remote sensing data, the temporal and spatial variations in ozone (O3) pollution, the sensitivity of O3, and its relationship with meteorological factors in Hainan Island were analyzed in this study. The results showed that the maximum daily 8-h moving mean (O3-8h) in western and northern cities in Hainan Island was higher than that in the central, eastern, and southern cities. O3-8h was the highest in 2015, and O3-8h exceeding the standard proportion was the largest in 2019. In addition, O3-8h was positively correlated with average temperature (P<0.1), sunshine duration (P<0.01), total solar radiation (P<0.01), atmospheric pressure, and average wind speed and was negatively correlated with precipitation (P<0.05) and relative humidity. The satellite remote sensing data showed that the tropospheric NO2 column concentration (NO2-OMI) and HCHO column concentration (HCHO-OMI) displayed opposite trends in Hainan Island from 2015 to 2020. Compared with those in 2015, NO2-OMI increased by 7.74% and HCHO-OMI decreased by 10.2% in 2020. Moreover, Hainan Island belongs to the NOx control area, and the FNR value exhibited a fluctuating downward trend in the past 6 years, with a trend coefficient and climatic trend rate of -0.514 and -0.123 a-1, respectively. A strong correlation was observed between meteorological factors and the FNR value of Hainan Island.

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea-land segmentation.

The Great Barrier Reef (GBR) is predicted to undergo its sixth mass coral bleaching event during the Southern Hemisphere summer of 2021-2022. Coral bleaching-level heat stress over the GBR is forecast to start earlier than any previous year in the satellite record (1985-present). The National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) near real-time satellite-based heat stress products were used to investigate early-summer sea surface temperature (SST) and heat stress conditions on the GBR during late 2021. As of 14 December 2021, values of instantaneous heat stress (Coral Bleaching HotSpots) and accumulated heat stress over a 12-week running window (Degree Heating Weeks) on the GBR were unprecedented in the satellite record. Further, 89% of GBR satellite reef pixels for this date in 2021 had a positive seven-day SST trend of greater than 0.2 degrees Celsius/week. Background temperatures (the minimum temperature over the previous 29 days) were alarmingly high, with 87% of GBR reef pixels on 14 December 2021 being greater than the maximum SST over that same 29-day period for any year from 1985-2020. The GBR is starting the 2021-2022 summer season with more accumulated heat than ever before, which could have disastrous consequences for the health, recovery, and future of this critical reef system.

UNASSIGNED: Air particulate matter with an aerodynamic diameter of 10 µm or less (PM10) is one of the main causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study explored the relationship between PM10 by remote sensing and AECOPD in Chaharmahal-o-Bakhtiari province from 2014 to2018.
UNASSIGNED: PM10 concentrations were predicted and validated based on aerosol optical depth (AOD) from 161 images processed by MODIS sensor and ground air quality monitoring station data. Demographic information and spirometric indices of 2038 patients with AECOPD were collected and analyzed from the hospital during the studied periods. SPSS software was used to analyze the relationships between these two categories of information.
UNASSIGNED: There was a significant negative relationship between PM10 and FVC, FVC%, FEV1, FEV1%, FEF25-75, FEV1/FVC, PEF, and FEF25FVC indices (p < 0.05). The results showed that over 2014-2018, the annual mean of PM10 concentrations varied from 35 to 52 µg/m3. The result of the regression model showed that the patient\'s age, body mass index (BMI), and PM10 concentrations were the most affecting variables on the two important spirometric indices i.e., FVC% and FEV1%. The PM10 concentrations and number of AECOPD patients had a similar pattern during the studied period. The women group, age group above 74 years, normal BMI, and non-smoking patients showed the most sensitivity to the PM10 concentrations.
UNASSIGNED: Our findings provide supplementary scientific information on PM10 concentration related to the incidence of AECOPD and as a variable affecting the most important spirometry indicators by providing local decision-makers information needed to set a priority of air pollution control measures as well as health services.

CO2 concentration (XCO2) shows the spatial and temporal variation in Iran. The major purpose of this investigation is the assessment of the spatial distribution of carbon dioxide concentration in the different seasons of 2013 based on the Thermal And Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) level 2 GOSAT data by implementing the ordinary kriging (OK) method. In this study, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS), and metrological parameters (temperature and precipitation) were used for the analysis of the spatial distribution of CO2 over Iran in 2013. The spatial distribution maps of XCO2 show the highest concentration of this gas in the south and south-east and the lowest concentration in the north and north-west. These results indicate that the concentration of carbon dioxide decreased with the increase of LST and temperature and a decrease of NDVI and humidity in the study area. Therefore, the existence of vegetation has an effective role in capturing carbon from the atmosphere by photosynthesis phenomena, and sustainable land management can be effective for carbon absorption from the atmosphere and mitigation of climate change in arid and semi-arid regions.

Mountain plants are considered among the species most vulnerable to climate change, especially at high latitudes where there is little potential for poleward or uphill dispersal. Satellite monitoring can reveal spatiotemporal variation in vegetation activity, offering a largely unexploited potential for studying responses of montane ecosystems to temperature and predicting phenological shifts driven by climate change. Here, a novel remote-sensing phenology approach is developed that advances existing techniques by considering variation in vegetation activity across the whole year, rather than just focusing on event dates (e.g. start and end of season). Time series of two vegetation indices (VI), normalized difference VI (NDVI) and enhanced VI (EVI) were obtained from the moderate resolution imaging spectroradiometer MODIS satellite for 2786 Scottish mountain summits (600-1344 m elevation) in the years 2000-2011. NDVI and EVI time series were temporally interpolated to derive values on the first day of each month, for comparison with gridded monthly temperatures from the preceding period. These were regressed against temperature in the previous months, elevation and their interaction, showing significant variation in temperature sensitivity between months. Warm years were associated with high NDVI and EVI in spring and summer, whereas there was little effect of temperature in autumn and a negative effect in winter. Elevation was shown to mediate phenological change via a magnification of temperature responses on the highest mountains. Together, these predict that climate change will drive substantial changes in mountain summit phenology, especially by advancing spring growth at high elevations. The phenological plasticity underlying these temperature responses may allow long-lived alpine plants to acclimate to warmer temperatures. Conversely, longer growing seasons may facilitate colonization and competitive exclusion by species currently restricted to lower elevations. In either case, these results show previously unreported seasonal and elevational variation in the temperature sensitivity of mountain vegetation activity.