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Lake Burullus is the second largest lake at the northern edge of the Nile Delta, Egypt, and has been recognized as an internationally significant wetland that provides a habitat for migrating birds, fish, herpetofauna, and mammals. However, the lake is experiencing severe human impacts including drainage and conversion to agricultural lands and fish farms. The primary goal of this study was to use multispectral, moderate-spatial-resolution (30 m2) Landsat satellite imagery to assess marsh loss in Lake Burullus, Egypt, in the last 35 years (1985–2020). Iterative Self-Organizing Data Analyses (ISODATA) unsupervised techniques were applied to the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager–Thermal Infrared Sensor (OLI–TIRS) satellite images for classification of the Lake Burullus area into four main land-use classes: water, marsh, unvegetated land surfaces (roads, paths, sand sheets and dunes), and agricultural lands and fish farms. The overall classification accuracy was estimated to be 96% and the Kappa index was 0.95. Our results indicated that there is a substantial loss (44.8% loss) in the marsh aerial coverage between 1985 and 2020. The drainage and conversion of wetlands into agricultural lands and/or fish farms is concentrated primarily in the western and southern part of the lake where the surface area of the agricultural lands and/or fish farms doubled (103.2% increase) between 2000 and 2020. We recommend that land-use-policy makers and environmental government agencies raise public awareness among the local communities of Lake Burullus of the economic and environmental consequences of the alarming loss of marshland, which will likely have adverse effects on water quality and cause a reduction in the invaluable wetland-ecosystem services.

To better assess the relationship between excess nutrient runoff and plant species diversity in the Egyptian northern coastal lakes, the relationships between aboveground biomass, species diversity, and both micro and macronutrient concentrations in sediment, water, and plant materials were investigated. A total of 38 sampling sites were established for the five Egyptian northern lakes (8 for Bardawil, 10 for Manzala, 8 for Burullus, and 6 for each of Edku and Mariut). Sediment, water, and plant materials were collected and analyzed for both micro and macronutrients including nitrogen (N), phosphorus (P), sulfur (S), magnesium (Mg), calcium (Ca), potassium (K), iron (Fe), boron (B), sodium (Na), and aluminum (Al). Based on the Sørensen similarity index, Burullus and Mariut lakes were very similar (0.70) in their vegetation composition, while Bardawil Lake had no similarity with the rest of the lakes. In sediment, Mariut Lake had the highest total P concentrations (1.3 g kg−1), while Bardawil Lake had the lowest (0.3 g kg−1). Bardawil, a hypersaline lake, had the highest concentrations for both Na and B (9.6 and 0.1 g kg−1, respectively). Among the deltaic lakes, Mariut Lake water bodies had the lowest plant species richness. The current study indicated that the excessive agricultural and industrial nutrient runoff had a greater impact on the nutrient distribution pattern and negatively impacted plant species diversity at the Egyptian coastal lakes. An integrated management plan, including establishing more pretreatment facilities for runoff and wastewater, should be implemented to reduce the nutrient loads from the main industrial and agricultural runoff sources. Moreover, periodic monitoring and assessment for nutrient runoff reaching the lakes are necessary to help reduce eutrophication levels.

Healthy coastal sabkhas (sabkha is an Arabic term for a salt flat) offer plenty of ecosystem services including climate change mitigation. However, fewer research studies were conducted at coastal sabkhas compared to other coastal marshes. This study was conducted in a total of ten coastal sabkha sites with different vegetation covers along the southern Red Sea coast of Saudi Arabia. The main objectives were to model and predict the distribution of volumetric soil organic carbon (SOC) density (kg C/m3) and cumulative SOC stocks (kg C/m2) using three different mathematic functions (allometric, exponential, and sigmoid) based on sampled and observed soil carbon (C) data (total of 125 soil cores = 1250 soil samples). Sigmoid function showed the greatest fit for predicting the distribution of volumetric SOC density over soil profile depth with mean Adj. R2 = 0.9978, 0.9611, and 0.9623 for vegetation cover of >25–50, >50–75, and >75–100%, respectively. For modeling the cumulative SOC stocks, both validation indices and p of the t-test confirmed that using the exponential function is the most appropriate to be used for predicting the SOC stock among different vegetation covers. Moreover, assessing the topsoil concentration factors (TCFs) showed that the distribution of the SOC content is impacted to a great extent by the vegetation cover at coastal sabkhas. Sampling the soil parameter of interest to estimate the SOC stocks is constrained by time and cost. Therefore, using the exponential function for predicting the distribution of cumulative SOC stocks at coastal sabkhas over soil profile depth is appropriate and promising for mapping SOC stocks at both regional and global spatial scales.