Browsing by Subject "Chlorophyll"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Monitoring Phytoplankton Biomass and Surface Temperatures of Small Inland Lakes by Multispectral and Thermal UAS Imagery
    (2021-08) Bartelt, Garrett
    Chlorophyll-a is an essential environmental indicator for water quality monitoring, as it is used in photosynthesis by all phytoplankton. Chlorophyll-a concentration in water is correlated to phytoplankton biomass, which is used to monitor harmful algal blooms (HABs). The eutrophication of waters observed during HABs can deplete the water of dissolved oxygen, smother aquatic vegetation, and some species can even release cyanotoxins into the environment. Nutrient pollution and warmer waters caused by climate change are expected to increase the intensity and frequency of HABs. Remote sensing chlorophyll-a concentration for HAB monitoring has been demonstrated with satellite imagery. Satellites effectively monitor temperatures and large algal blooms in oceans and large lakes but lack the spatial resolution to monitor small bodies of water effectively. This study aims to apply remote sensing techniques to multispectral and thermal images captured by an unmanned aerial system (UAS). A UAS survey was conducted on a small freshwater lake, Brownie Lake, in Minneapolis, Minnesota. The collected imagery was then correlated to in-situ chlorophyll-a and temperature measurements. Data was collected using the MicaSense Altum sensor. The Altum is a combination multispectral and thermal camera designed for agricultural data collection. While the multispectral camera attachment is not designed for surface water surveys, this study observed good agreement between measured chlorophyll-a concentrations in a small freshwater lake and the UAS multispectral data. Chlorophyll-a concentration was discovered to be highly correlated with the indexes containing the near-infrared (NIR) band, with a wavelength of 840 nm. Of the multispectral indexes evaluated in this study, the most correlated index was the normalized difference vegetative index (NDVI), with an R2 value of 0.80. Remotely determined water surface temperatures also showed a correlation to manually collected water temperatures. This correlation was not as definitive, with an experimental R2 value of 0.31. This research supports the use case for UAS technologies in measuring water quality indicators important to HAB monitoring, such as temperature and chlorophyll-a concentration.
  • Thumbnail Image
    Item
    Remote sensing for regional assessment and analysis of Minnesota lake and river water quality
    (2012-05) Olmanson, Leif Gordon
    Beginning soon after the launch of the first Landsat satellite, researchers began investigating the use of Landsat imagery to monitor the water quality of our lakes and coastlines. The earliest use of Landsat imagery was for simple qualitative observations which included locating and mapping pollution and pollution plumes. Shortly thereafter, field measurements of water quality were correlated with Landsat data and later these correlations were used for quantitative assessment of water quality (e.g., turbidity, chlorophyll and water clarity). This dissertation expands on this earlier work and describes results of research to develop and use remote sensing tools for regional water quality assessment to improve the understanding and management of Minnesota's lakes and rivers. It includes four major components. First, a 20-year, 1985-2005, comprehensive water clarity database for more than 10,500 lakes at approximately five-year intervals for the time period 1985-2005, which includes almost 100,000 individual estimates of lake water clarity, was compiled and evaluated. Second, the results of a statistical analysis of the Landsat database for geospatial and temporal trends of water clarity over the 20-year period, as well as trends related to land cover/use and lake morphometry, are reported. Third, the advantages of improved spectral and temporal resolution and disadvantages of the lower spatial resolution of the global MODIS and MERIS systems are evaluated for regional-scale measurements of lake water clarity and chlorophyll of large lakes in Minnesota and compared with Landsat. Finally, aerial hyperspectral spectrometers were used to collect imagery with high spatial and spectral resolution for use in identifying, measuring and mapping optically related water quality characteristics of major rivers in Minnesota for three time periods that represent different water quality and flow regimes.
  • Thumbnail Image
    Item
    Spectral Detection of Soybean Aphid (Hemiptera: Aphididae) and Confounding Insecticide Effects in Soybean
    (2017-01) Alves, Tavvs
    Soybean aphid, Aphis glycines (Hemiptera: Aphididae) is the primary insect pest of soybean in the northcentral United States. Soybean aphid may cause stunted plants, leaf discoloration, plant death, and decrease soybean yield by 40%. Sampling plans have been developed for supporting soybean aphid management. However, growers’ perception about time involved in direct insect counts has been contributing to a lower adoption of traditional pest scouting methods and may be associated with the use of prophylactic insecticide applications in soybean. Remote sensing of plant spectral (light-derived) responses to soybean aphid feeding is a promising alternative to estimate injury without direct insect counts and, thus, increase adoption and efficiency of scouting programs. This research explored the use of remote sensing of soybean reflectance for detection of soybean aphids and showed that foliar insecticides may have implications for subsequent use of soybean spectral reflectance for pest detection. Chapter 1 was the first publication showing that feeding from soybean aphid affects soybean spectral reflectance. Using ground-based spectroradiometers at canopy-level, it was found that soybean aphids affected plant reflectance at a narrowband wavelength within the near-infrared spectral range (800 nm), but had no effect at a red narrowband wavelength (680 nm). Soybean aphids also affected a vegetation index referred to as NDVI (i.e., normalized difference vegetation index), which combines the near-infrared and red wavelengths into a value representing photosynthetic pigment content and potential ultrastructure changes in soybean leaves. In general, soybean aphids induced similar effects on canopy- and leaf-level spectral measurements, but there were a few instances that significant effects at leaf-level were not detected by canopy-level. Chapter 2 used hyperspectral data and simulated wide-band sensors for detection of soybean aphid. While the first chapter showed that remote sensing is a promising solution based on results from two narrowband wavelengths, the second chapter provided an extensive search for band sensors that could optimize the use of soybean canopy reflectance for soybean aphid detection. Akaike’s Information Criteria (AIC) was used to rank and select sensors. Lower AIC values were considered to provide better models. The subset of narrowband wavelengths that optimized estimation of soybean aphid abundance was similar to that obtained using simulated wide-band sensors. Increasing sensor bandwidth corresponded to larger AIC values (worse models). The smallest AIC values (better models) were observed with narrow- and wide-band sensors centered around 780 nm. Chapter 3 assessed effects of foliar insecticides on spectral response of soybean plants under greenhouse and field conditions. Such effects could potentially confound measures of pest-induced spectral changes. Representatives of the two most commonly used insecticides (i.e., chlorpyrifos and λ-cyhalothrin) and a novel active ingredient referred to as sulfoxaflor affected soybean leaf reflectance. λ-cyhalothrin had the least effect on spectral reflectance and only affected a few near-infrared wavelengths, but sulfoxaflor and chlorpyrifos affected leaf reflectance at several visible and near-infrared wavelengths. I speculated that foliar insecticides had immediate effects via surface residues on plants and delayed effects via morpho-physiological changes induced by the insecticides. The potential leaf surface residues had transitory effects on soybean reflectance and no consistent pattern of spectral changes was associated with the insecticides. Overall, my results hold promise to identify and characterize injury of soybean aphid using remote sensing of soybean canopy reflectance. The information provided in this research may help to design optimized sensors for soybean aphid detection and contribute to the understanding of insect- and insecticide-induced effects on plants. It may also improve the current field-wide management tactics by making decisions for pest control when plant spectral reflectance indicates that soybean aphid abundance reached its economic threshold. To incorporate remote sensing into IPM programs, this new scouting method based on plant spectral reflectance will need further research to adjust economic thresholds, application of insecticides with no or short-duration effects on plant spectral data, and better understanding of other plant-pest interactions affecting plant morpho-physiology. It will be important to distinguish spectral changes induced by soybean aphid from other confounding factors such as other herbivores, nutritional deficiencies, diseases, and water stress. Future research will be needed to determine if the ground-based effects documented in our studies can be detected from space- and air-based platforms, such as satellites and unmanned aerial systems. Moreover, advancing our results may contribute to determine where and when insecticides are needed by using the spatial location of soybean spectral responses to soybean aphid infestations. Remote sensing has the potential to expand the use of IPM practices and collaborate to the mission of feeding an increasing population that has been changing diet habits and will require more production of food.