Sea water quality monitoring using remote sensing techniques: a case study in Tangier-Ksar Sghir coastline

Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine

In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of - 0.65 and - 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources.

Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring

The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China

Based on MODIS EVI data of August collected from 2010 to 2021, and taking the Yingpanhao coal mine as an example, the spatiotemporal variation features of vegetation are analyzed using time series analysis, trend analysis and correlation analysis methods in the eco-geo-environment of the phreatic water desert shallows oasis. A significant increase trend is found for vegetation variation, and its development has improved generally in most areas. There is an obvious positive correlation between precipitation and vegetation growth, and a negative correlation between coal mining intensity and vegetation growth, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity in the eco-geo-environment. The research results effectively reflect that atmospheric precipitation is the primary factor advancing the vegetation growth status in the coal mining regions. Vegetation development response to coal mining would be degraded first, then improved, and finally restored in areas with a deeply buried phreatic water level; that would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants in areas with a shallowly buried phreatic water level. Therefore, it is necessary to carry out reasonable mine field planning according to the phreatic water level and the vegetation type distribution and to adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation to avoid damaging the original hydrogeological conditions as far as possible. This information is helpful for promoting the eco-geo-environmental protection and further establishing the need for the dynamic monitoring of the eco-environment in the coal mining regions in the arid and semi-arid ecologically vulnerable areas of Northern China, which play a significant role in the long-term protection and rehabilitation of the eco-geo-environment and in the promotion of sustainable development.