Spatiotemporal Analysis Of Lake Water Quality Indicators On Small Lakes, Lake Bloomington And Evergreen Lake In Central Illinois, Using Satellite Remote Sensing

This research explores the use of Sentinel-2 satellite to determine the spatiotemporal patterns of lake water quality indicators (e.g. chlorophyll a) in Lake Bloomington and Evergreen Lake. Lake water quality issues related to algal blooms is a serious problem in basins with abundant agricultural lands causing harmful effects to freshwater ecosystems such as pollution of beaches, taste and odor problems in drinking water, depletion of oxygen levels causing fish kills and the issue of water exceeding safe drinking water standards. Developing monitoring techniques using various water quality indicators of algal blooms is crucial. In this project, remote sensing and field sampling methods were employed to assess the state of water quality of two small lakes, Lake Bloomington and Evergreen Lake, in Central Illinois. Water samples were collected from selected locations from the lakes to test for various water quality variables including nitrate, phosphorus and chlorophyll a. An exo sonde instrument and secchi disk was used to measure additional water quality parameters such as turbidity, secchi depth, and temperature. Concurrent satellite images obtained from Sentinel-2 with flyover with ±5 days were processed and analyzed, and the results were compared with field sampling data. Single and multiple pixel analyses were conducted on various algorithms such as Bottom-of-Atmosphere (BOA), Maximum Chlorophyll Index (MCI), and band ratios. These algorithms were tested to identify the best algorithm for estimating water quality parameters using satellite data for the two lakes. A regression analysis was conducted to derive a linear model which was used to create water quality indicator maps that showed the spatial pattern of algae in the lakes. From the results of the research, Lake Bloomington was more turbid and had higher concentrations of chlorophyll a than Evergreen Lake. Except for band ratio of B1/B2 of Sentinel-2 data, a poor regression relationship between satellite and field water quality values was observed for Lake Bloomington. This poor relationship could be due to the high turbidity of the lake. Evergreen Lake, on the other hand, showed a stronger relationship between satellite values and chlorophyll a. Generally, spatial analysis reveals that chlorophyll a distribution was heterogeneous, and it increased from downstream areas to upstream areas

SPATIAL PATTERNS OF ALGAL BLOOMS IN LAKE BLOOMINGTON AND EVERGREEN LAKE

Fresh water is one of the most important sources of drinking water for the United States population and when our water is polluted, it is not only devastating to the environment but also to human health. Algal blooms can cause harmful effects to freshwater ecosystems such as pollution of beaches, taste and odor problems in drinking water, and depletion of oxygen levels causing fish kills. They can have negative effects on the health of humans as well as other animals who use them for drinking or recreation. Algal blooms have been a growing water pollution problem in the Midwest, causing contamination of major reservoirs from which cities and towns draw drinking water. Algal blooms occur in freshwater when there is a sudden rise in the population of algae found in the water body and it causes the color of the water to change. The objective of this research project is to examine the spatial patterns of algal blooms as well as their effect on water quality in Lake Bloomington and Evergreen Lake - the two reservoirs from which the City of Bloomington draws its water for water supply. The Bloomington water-supply system currently supplies over 80,000 people in the city of Bloomington, Hudson & Towanda Townships and half of the population of Dale and Dry Grove townships. This project explores the effects of algal blooms in water and the environment by using remote sensing and field work data to monitor algal bloom occurrence. Methods that are transferable and will enable the determination of algal bloom occurrence at other locations will be developed. Monitoring of lakes using satellite remote sensing data is useful in estimating and detecting water quality problems that would have gone undetected in lakes. Water samples will be collected from selected locations on the lakes to test for various water properties such as nitrate, phosphate, chlorophyll a, etc. A function derived from regression analysis conducted alongside with models/maps created will be used to predict water quality of the other locations of the lake not sampled. Results have shown that blooms occur at different times of the year in each lake e.g. August for Evergreen Lake, October for Lake Bloomington. Using satellite image reflectance data from Landsat 8 images, we expect to see spatial patterns in water quality

Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: a study case from northern Morocco

In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling (cLHS)] and sample ratios (10% = 147 and 25% = 368) to predict soil phosphorus and potassium contents, previously estimated using standard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better

Estimating soil organic matter: a case study of soil physical properties for environment-related issues in southeast Nigeria

The different deposition periods in sedimentary geological environment have made the build-up and estimation of soil organic matter ambiguous to study. Soil organic matter has received global attention in the ambience of international policy regarding environmental health and safety. This research was to understand the inter-relationship between soil organic matter and bulk density, saturated hydraulic conductivity (Ksat), total, air-filled and capillary porosities for organic matter estimation, via different multiple linear regression functions (i.e., leapbackward, leap forward, leapseq and lmStepAIC), in soils developed over the sedimentary geological environment. Eight mapping units were obtained in Ishibori, Agoi Ibami and Mfamosing via digital elevation model. Two pits were sited within each mapping unit, and 53 soil samples were used for the study. In soils over shale–limestone–sandstone, two pits were sited, six in alluvium, four in sandstone–limestone and four in limestone. Overall correlation between SOM with Ksat (r = 0.626) and BD (r = − 0.588) was significant (p < 0.001). The strongest correlation was obtained for SOM with BD (r = − 0.783) and Ksat (r = 0.790) in soils over limestone. In contrast, soils over shale–limestone and sandstone geological environment gave the weakest relationship (r < 0.6). Linear regression gave a similar prediction output. The best performing was leapbackward (RMSE = 11.50%, R2 = 0.58, MAE = 8.48%), which produced a smaller error when compared with leap forward, leapseq and lmStepAIC functions in organic matter estimation. Therefore, we recommend applying leapback linear regression when estimating soil organic variation with physical soil properties for solving soil–environmental issues towards sustainable crop production in southeast Nigeria