Change detection for Lake Burullus, Egypt using remote sensing and GIS approaches

Water quality assessment of Lake Burullus, Egypt, utilizing statistical and GIS modeling as environmental hydrology applications

GIS is a very powerful tool for analyzing huge amount of data and connecting them with the geography; moreover, recently, there is great advancement in the field. The main objective of this study is to assess the water quality (WQ) and trophic status (TS) conditions of Lake Burullus, Egypt, using statistical modeling (PCA/FA and CA), WQ index (L-WQI), and trophic status index (Carlson TSI and TRIX) approaches, in addition to using GIS tools for building models able to automatically calculate the various indices and producing color coded maps for the lake. The results indicated that PCA/FA grouped the twenty-four WQ parameters into nine principal components explaining 72.6% of the total variance, domestic, and agriculture pollution were dominant. CA divided the twelve sampling stations into most and least polluted groups. The lake WQ was classified as a "Very Poor," according to L-WQI. Moreover, the results of the Carlson TSI and TRIX indices were coincided and classified the eutrophication levels in the lake as "Hyper-Eutrophic" and "Elevated Trophic," respectively. Based on the results of this study, Lake Burullus needs urgent plans for recovering its WQ. Pre-treatment for its drains' effluents and implementing of a periodical WQ monitoring program are highly recommended.

Remote Sensing of Sediment Discharge in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms

The spatio-temporal dynamism of sediment discharge (Qs) in rivers is influenced by various natural and anthropogenic factors. Unfortunately, most rivers are only monitored at a limited number of stations or not gauged at all. Therefore, this study aims to provide a remote-sensing-based alternative for Qs monitoring. The at-a-station hydraulic geometry (AHG) power–law method was compared to the at-many-stations hydraulic geometry (AMHG) method; in addition, a novel AHG machine-learning (ML) method was introduced to estimate water discharge at three gauging stations in the Tisza (Szeged and Algyő) and Maros (Makó) Rivers in Hungary. The surface reflectance of Sentinel-2 images was correlated to in situ suspended sediment concentration (SSC) by support vector machine (SVM), random forest (RF), artificial neural network (ANN), and combined algorithms. The best performing water discharge and SSC models were employed to estimate the Qs. Our novel AHG ML method gave the best estimations of water discharge (Szeged: R2 = 0.87; Algyő: R2 = 0.75; Makó: R2 = 0.61). Furthermore, the RF (R2 = 0.9) and combined models (R2 = 0.82) showed the best SSC estimations for the Maros and Tisza Rivers. The highest Qs were detected during floods; however, there is usually a clockwise hysteresis between the SSC and water discharge, especially in the Tisza River.

Loss of Coastal Wetlands in Lake Burullus, Egypt: A GIS and Remote-Sensing Study

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.

Assessment of Water Quality, Eutrophication, and Zooplankton Community in Lake Burullus, Egypt

Burullus Lake is Egypt’s second most important coastal lagoon. The present study aimed to shed light on the different types of polluted waters entering the lake from various drains, as well as to evaluate the zooplankton community, determine the physical and chemical characteristics of the waters, and study the eutrophication state based on three years of seasonal monitoring from 2017 to 2019 at 12 stations. The results revealed that Rotifera, Copepoda, Protozoa, and Cladocera dominated the zooplankton population across the three-year study period, with a total of 98 taxa from 59 genera and 10 groups detected in the whole-body lake in 2018 and 2019, compared to 93 species from 52 genera in 2017. Twelve representative surface water samples were collected from the lake to determine physicochemical parameters, i.e., temperature, pH, salinity, dissolved oxygen, biological oxygen demand, chemical oxygen demand, ammonia-N, nitrate–N, nitrate-N, total nitrogen, total phosphorus, dissolved reactive phosphorus, and chlorophyll-a, as well as Fe, Cu, Zn, Cr, Ni, Cd, and Pb ions. Based on the calculations of the water quality index (WQI), the lake was classified as having good water quality. However, the trophic state is ranked as hyper-eutrophic and high trophic conditions.

Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen's KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.

Water quality monitoring of Lake Burullus (Egypt) using Landsat satellite imageries

Lake Burullus is one of the most important coastal lakes in Egypt, as it participates with a considerable amount of fish yield in Egypt. Despite its importance, it is considered as a vulnerable lake, since it is subjected to significant environmental changes caused by various anthropogenic activities. Severe deterioration of its water quality status, as well as a decrease in its water area, has been noticed throughout the last four decades. The main objective of this study is to evaluate the ability of remote sensing for assessing the water quality status of the lake for the period from August 2010 to August 2013. Remote sensing technique was used to retrieve and predict the lake water quality parameter records through the assessment period (August 2010 to August 2013). Stepwise multi-linear regression technique was used to correlate between the field measurements of water quality parameters and the reflectance of remote sensing imageries, and then the derived models were validated. The results revealed the critical status of water quality conditions of the lake, particularly of its southern and southeastern parts. The results showed also that some water quality parameters (Chl-a, TSS, pH, Fe, Zn, Cr, and NH₄) can be retrieved from remote sensing imageries with reasonable accuracy (R² = 0.86, 0.67, 0.65, 0.63, 0.62, 0.61, and 0.6, respectively), while the water quality parameters, which can be predicted, based on calibrated and validated regression models are TSS and Chl-a, with acceptable accuracy (R² = 0.6 and 0.43, respectively). Lake Burullus needs urgent plans and strategies to protect its water quality from the potential hazards of human activities. The study proved the ability of remote sensing as an effective technique to monitor the changes in water quality conditions of shallow coastal lagoons and to predict with some water quality parameters without field measurements; therefore, it is highly recommended to be used by decision makers.

Environmental monitoring and prediction of land use and land cover spatio-temporal changes: a case study from El-Omayed Biosphere Reserve, Egypt

Environmental monitoring, using the techniques of remote sensing (RS) and geographic information systems (GIS), allows the production of time efficient, cost-effective, and reliable surveillance and tracking data. Anthropogenic activities appear to be the major trigger of environmental changes, including land use and land cover (LULC) changes, while natural causes have only a minor impact in most cases. The Omayed Biosphere Reserve (OBR) stands as one of the Egyptian protected areas most highly affected by massive unplanned human activities. Thus, the main objective of this study is to determine the spatio-temporal changes in the OBR over a 35-year period using five Landsat (5 ETM images and 8 OLI-TIRS) imageries, with the specific aim of measuring change rates, trends, and magnitudes of LULC changes between 1984 and 2019 with the topography for planning and selection of developmental strategies. The Normalised Difference Vegetation Index is used to identify the vegetation characteristics of different eco-regions and delivers useful information for the study of vegetation health and density. Normalised Difference Built-up Index can likewise be used to quote built-up areas. Unsupervised classification was used to classify LULC patterns. Six classes were recognised: water bodies, coastal sand, urban areas, cultivated land, newly reclaimed areas, and bare soil. Our results reveal that about 33.55% of OBR land cover has transformed into other forms. Cultivated land and urban regions increased by about 143.5 km² and 56.17 km² from 1984 to 2019, respectively. Meanwhile, bare soil decreased to around 209.5 km² in 2019. In conclusion, the conversion of bare soil into urban land and cultivated areas is the major change in the last 35 years in the OBR. Over the past three decades, the OBR has faced radical and imbalanced changes in its natural habitats. Therefore, monitoring and management of LULC changes are crucial for creating links between policy decisions, regulatory actions, and following LULC activities in the future, especially as many potential risks still exist in the remaining regions of the OBR.