MENVSC / Department of Ecology and Resource Management / Satellite remote sensing techniques have been proved to be the best methods for quantifying chlorophyll-a levels by estimating algal concentrations in water bodies. Harmful algal blooms (HABs) are posing a significant threat to the many water bodies in South Africa. This study aims at developing remote sensing solution to estimate chlorophyll concentrations in water bodies of Vhembe district municipality using recently launched Landsat 8 OLI. It is the first study to provide quantitative water quality information for the Vhembe region’s water bodies from a time series of satellite remotely sensed data and in-situ laboratory data. The objectives of this study was to evaluate spatial and temporal distributions of algae in water bodies and fish-ponds, to assess water quality parameters, namely: chlorophyll-a and turbidity and to compare data obtained from satellite remote sensors with in situ data. The 30 meters spatial resolution multispectral Landsat 8 OLI for 2016, 2017 and 2018 were used to derive chlorophyll-a estimate from an existing model at three water bodies. The chlorophyll-a concentrations measured during in-situ were employed to validate the Landsat derived chlorophyll-a estimates. The results from this study shows that Landsat derived chlorophyll-a estimates are correlating with field measurements. In all the reservoir, it was detected that there is low content of harmful algal blooms and thus the water bodies are in good condition since the chlorophyll-a concentrations were very low (ranging from 0 to 0.6 mg.m-3). In conclusion, it can be stated that Landsat 8 OLI sensor has the potential to map inland water bodies dominated with algal blooms at certain extent. It can further be stated that Landsat 8 OLI is suitable for monitoring the growth of HABs in aquatic ecosystem and is cost effective. This study also evaluated the potential of Banana peels powder and K2SO4 to inhibits the growth of algae (batch experiment). The water samples were collected at Tshifulanani and Lwamondo fish ponds where there are floating algae. The samples were collected seasonally and analysed for pH, water temperature, Total Dissolved Solids, Electrical conductivity, Dissolved Oxygen, turbidity, chlorophyll-a and absorbance. From the laboratory experiments, there was a variation in the values of absorbance (0.936A-1.234A), PH (7.1-8.3), EC (63.1- 87.9 μs/cm), TDS (52.6-69.7mg/L), water temperature (25.5-29.3°C) and Dissolved oxygen (5.3-7.1mg/L). The concentration of chlorophyll-a for Tshifulanani and Lwamondo fish ponds ranges were (2.14-15.96 mg/m-3) and (0.65-15.66 mg/m-3) respectively. A batch experiment was conducted to determine the potential of banana peels powder on inhibition of algal blooms in water samples by measuring absorbance at 750nm. It can be concluded in this study that banana peels have a potential to inhibits the growth of algae in fish ponds. The Absorbance has shown a rapid v decrease from 0.936A to Zero from day 1 to day 7 respectively. The inhibition of cyanobacteria by banana peels is followed using Potassium sulphate in treating the algal blooms in water samples. Both banana peels and potassium Sulphate has shown a positive response in treatment of algae on the batch experiment. The results of this study revealed that high concentration of physico-chemical parameters promote the growth of cyanobacteria in fish ponds but does not have negative effects on the fish except the oxygen competition with algal blooms. The statistical analysis in correlating the chl-a field measurements and remotely sensed data showed a positive outcome where K values were very high from 70% to 89%. These results show high level of agreement of correlation values of field chlorophyll-a concentration and satellite remotely sensed variables. / NRF

The master thesis deals with the determination of the chlorophyll content in birch foliage (Betula pendula Roth) and Scots pine using hyperspectral data. The first part of the thesis concentrates on the literature search dealing with the methods of chlorophyll content in the foliage of selected plant species. In the practical part the emphasis is on the study of spectral reflectance curves and finding their relation to the chlorophyll content from the laboratory determination. Images taken with the hyperspectral sensor HyMap and spectral reflectance curves obtained with the ground ASD FieldSpec 3 spectrometer were available. Using the derived regression model chlorophyll maps were created for Scots pine for three selected locations in the Sokolov coal basin area.

Iron deficiency chlorosis (IDC) is the most common reason for chlorosis in soybean (Glycine max (L.) Merrill) and causes an average yield loss of 120 million dollars per year across 1.8 million ha in the North Central US alone. As the most effective way to avoid IDC is the use of tolerant cultivars, they are visually rated for IDC by experts; however, this method is subjective and not feasible for a larger scale. An alternate more objective image processing method can be implemented in various platforms and fields. This approach relies on a color vegetation index (CVI) that can quantify chlorophyll, as chlorophyll content is a good IDC indicator. Therefore, this research is aimed at developing image processing methods at leaf, plot, and field scales with machine learning methods for chlorophyll and IDC measurement. This study also reviewed and synthesized the IDC measurement and management methods. Smartphone digital images with machine learning models successfully estimated the chlorophyll content of soybean leaves infield. Dark green color index (DGCI) was the best-correlated CVI with chlorophyll. The pixel count of four different ranges of DGCI (RPC) was used as input features for different models, and the support vector machine produced the highest performance. Handheld camera images of soybean plots extracted DGCI, which mimicked visual rating, and canopy size that were used as inputs to decision-tree based models for IDC classification. The AdaBoost model was the best model in classifying IDC severity. Unmanned aerial vehicle soybean IDC cultivar trial fields images extracted DGCI, canopy size, and their product (CDP) for IDC severity monitoring and yield prediction. The area under the curve (AUC) was employed to aggregate the data into a single value through time, and the correlation between all the features and yield was good. Although CDP at latest growth stage had the highest correlation with yield, AUC of CDP was the most consistent index for soybean yield prediction. This research demonstrated that digital image processing along with the machine learning methods can be successfully applied to the soybean IDC measurement and the various soybean related stakeholders can benefit from this research.

Carried out in collaboration with the Occoquan Water Monitoring Lab, this thesis presents the results of research that sought to ascertain the spatial distribution of chlorophyll-a concentrations in the Lake Manassas Reservoir using a combination of Landsat TM satellite imagery and ground based field measurements. Images acquired on May 14, 1998 and March 8, 2000 were analyzed with chlorophyll-a measurements taken on 13, 1998 and March 7, 2000. A ratio of Landsat TM band 3: Landsat Band 4 was used in a regression with data collected at eight water quality monitoring stations run by the Occoquan Watershed Monitoring Lab. Correlation coefficients of 0.76 for the 1998 data and 0.73 for the 2000 data were achieved. Cross validation statistical analysis was used to check the accuracy of the two models. The standard error and error of the estimate were reasonable for the models from both years. In each instance, the ground data was retrieved approximately 24 hours before the Landsat Image acquisition and was a potential source of error. Other sources of error were the small sample size of chlorophyll-a concentration measurements, and the uncertainty involved in the location of the water quality sampling stations. / Master of Science

Large public events, such as concerts, rallies, and festivals, impact turf health when held on natural turfgrass surfaces. The impact associated with these events is due to the placement of physical structures such as stages and seating areas and pedestrian and vehicular traffic on the turf surface. Trafficked turf protection covers, which are field covers meant to be placed directly on the turf surface where pedestrian or vehicular traffic is expected and/or equipment will be placed, can be used to minimize damage to the turf surface. Scientific data on turf response to these covers is lacking. Four cover treatments comprised of a non-covered non-trafficked control, plywood, plywood + Enkamat Plus, and white high-density polypropylene [single sided (Terratile) or double sided (Matrax)] were applied to tall fescue (Festuca arundinacea Schreb.) and effects of light intensity, duration of covering, season and soil moisture were evaluated. Growth chambers and field experiments were conducted in 2010-2011. Tissue samples were taken in growth chambers experiments every four days over the 20-day period to analyze chlorophyll (Chl a, Chl b, Chl a+b) and carotenoids (carot) under split factors of light intensity (12hr, PAR 530 μmol m-2 s-1, 5 μmol m-2 s-1) and soil moisture (50%, 75% of pot soil moisture capacity). Field trial treatment effects were observed every two days and eight days after cover removal in the spring, summer and fall and a normalized difference vegetative index (NDVI) measure was used at the conclusion of each trial period to confirm visual ratings. Covers that allowed light transmission to the canopy provided the best visual retention of percent green cover and higher contents of Chl a, Chl b, Chl a+b and carot. However, when treatments were tested under conditions that simulated low light under a concert stage (PAR 5 μmol m-2 s-1), covers performed similarly. Moderate soil moisture increased Chl b and carot content under covers. Field trials showed that plywood and plywood + Enkamat allowed for acceptable covering periods of six days in spring, four days in fall, and zero days in summer. Summer conditions shortened the number of days (8 -10) thattall fescue could be covered with Matrax and Terratile and still maintain an acceptable level of green cover. Matrax performed the best during high temperatures and did not tend to sink into the turf in saturated soil. All covers exhibited desirable qualities and limitations that should be considered for turf protection during an event. / Master of Science

This study examined the usefulness of remote sensing to monitor peatlands, and more specifically Sphagnum moss ‘health’. Results from this study show that thermal imaging can be used to monitor Sphagnum productivity, as when the surface temperature of Sphagnum exceeds a threshold value (30.8 °C in the field and 18.2 °C in the laboratory), Sphagnum quickly changes from being productive to being unproductive. The Enhanced Normalized Difference Vegetation Index (ENDVI) can also be used in a similar manner, where if the ENDVI value is high (above 0.11 in the field and -0.12 in the laboratory), Sphagnum will be productive, and otherwise, it will be stressed. A classification scheme was developed to monitor peatland recovery to fire disturbance. By utilizing the ENDVI, leaf area index and aboveground biomass within a recovering peatland can be mapped, as well as the recovery trajectory of the groundcover. The findings of this study highlight the potential use of remote sensing to assess the driving factors of Sphagnum moss stress, as well as quickly and expansively aid in peatland recovery trajectory. / Thesis / Master of Science (MSc)

No description available.

Globally, water bodies are increasingly affected by undesirable harmful algal blooms. This dissertation contributes to research methodology pertaining to quantification of the algal blooms in multiple water bodies of Mississippi using in situ, analytical, and remote sensing techniques. The main objectives of this study were to evaluate the potential of several techniques for phytoplankton enumeration and to develop remote sensing algorithms for several sensors and evaluate the performance of the sensors for quantifying phytoplankton in several water bodies. Analytical techniques such as “FlowCam”, an imaging flow cytometer; “HPLC”, high performance liquid chromatography with the chemical taxonomy program “ChemTax”; spectrofluorometric analyses; and “ELISA” assay were used to quantify a suite of parameters on algal blooms. Additionally, in-situ algal pigment biomass was measured using fluorescence probes. It was found that that each technique has unique potential. While some of the rapid and simpler techniques can be used instead of more involved techniques, sometimes use of several techniques together is beneficial for managing aquatic ecosystems and protecting human health. Algorithms were developed to quantify chlorophyll a using five remote sensing sensors including three currently operational satellite sensors and two popular sensors onboard the Unmanned Aerial Systems (UASs). Empirical band ratio algorithms were developed for each sensor and the best algorithms were chosen. Cluster analysis helped in differentiating the water types and linear regression was used to develop algorithms for each of the water types. The UAS sensor- Micasense was found to be most useful among the UAS sensors and the best overall with highest R2 value 0.75 with p<0.05 and minimum %RMSE of 28.22% and satellite sensor OLCI was found to be most efficient among the three satellite sensors used in the study for chlorophyll a estimation with R2 of 0.75 with p<0.05 and %RMSE 13.19%. The algorithms developed for these sensors in this study represent the best algorithms for chlorophyll a estimation in these water bodies based on R2 and %RMSE. The applicability of the algorithms can be extended to other water bodies directly or the approach developed in this study can be adopted for estimating Chl a in other water bodies.

No description available.

No description available.