Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

Local and landscape factors influencing mercury distribution in water, bottom sediment, and biota from lakes of the Araguaia River floodplain, Central Brazil

Mercury (Hg) is a chemical element widely present in the Earth's crust. However, its high toxicity and ability to accumulate in organisms and biomagnify through food chains characterize it as a global pollutant of primary control. We assessed total mercury concentrations ([THg]) in abiotic and biotic compartments from 98 floodplain lakes associated with the Araguaia River and six tributaries (Midwest Brazil). [THg] quantification in water was performed by cold vapor atomic fluorescence spectroscopy. [THg] in bottom sediment was assessed using cold vapor generation atomic absorption spectrophotometry, while [THg] in macrophyte, periphyton, and plankton were quantified by thermal decomposition atomic absorption spectrometry. Hotspots of [THg] in water, bottom sediment, and macrophytes were determined in areas impacted by pasture and urban areas. In contrast, hotspots of [THg] in periphyton and forest fires were determined in preserved areas downstream. [THg] in plankton did not show a clear spatial distribution pattern. The mean bioaccumulation factor order was plankton (2.3 ± 1.8) > periphyton (1.3 ± 0.9) > macrophytes (0.7 ± 0.4) (KW = 55.09, p < 0.0001). Higher [THg] in water and bottom sediment were associated with high pH (R2adj = 0.118, p = 0.004) and organic matter (R2adj = 0.244, p < 0.0001). [THg] in macrophytes were positively influenced by [THg] in water (R2adj = 0.063, p = 0.024) and sediment (R2adj = 0.105, p = 0.007). [THg] in periphyton are positively related to forest fires (R2adj = 0.156, p = 0.009) and [THg] in macrophytes (R2adj = 0.061, p = 0.03) and negatively related to lake depth (R2adj = 0.045, p = 0.02). The transfer of Hg from water and sediment to the biota is limited. However, the progressive increase of the bioaccumulation factor between macrophyte, periphyton, and plankton may indicate Hg biomagnification along the food chain of the Araguaia River floodplain.

A cloud-integrated GIS for forest cover loss and land use change monitoring using statistical methods and geospatial technology over northern Algeria

Over the last two decades, forest cover has experienced significant impacts from fires and deforestation worldwide due to direct human activities and climate change. This paper assesses trends in forest cover loss and land use and land cover changes in northern Algeria between 2000 and 2020 using datasets extracted from Google Earth Engine (GEE), such as the Hanssen Global Forest Change and MODIS Land Cover Type products (MCD12Q1). Classification was performed using the pixel-based supervised machine-learning algorithm called Random Forest (RF). Trends were analyzed using methods such as Mann-Kendall and Sen. The study area comprises 17 basins with high rainfall variability. The results indicated that the forest area decreased by 64.96%, from 3718 to 1266 km², during the 2000-2020 period, while the barren area increased by 40%, from 134,777 to 188,748 km². The findings revealed that the Constantinois-Seybousse-Mellegue hydrographic basin was the most affected by deforestation and cover loss, exceeding 50% (with an area of 1018 km²), while the Seybouse River basin experienced the highest percentage of cover loss at 40%. Nonparametric tests showed that seven river basins (41%) had significantly increasing trends of forest cover loss. According to the obtained results, the forest loss situation in Algeria, especially in the northeastern part, is very alarming and requires an exceptional and urgent plan to protect forests and the ecological system against wildfires and climate change. The study provides a diagnosis that should encourage better protection and management of forest cover in Algeria.

GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatial-temporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively; (2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively; and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be widely adopted to drive the RS-DHM to more quickly and easily provide reliable hydrological processes simulation results for integrated water resource management, therefore achieving win-win results in terms of efficiency and accuracy.