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Browsing by Author "Juzwik, Jennifer"

Now showing 1 - 6 of 6
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    Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination
    (2022-04-26) Sapes, Gerard; Lapadat, Cathleen; Schweiger, Anna K.; Juzwik, Jennifer; Montgomery, Rebecca; Gholizadeh, Hamed; Townsend, Philip A.; Gamon, John A.; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, Jeannine
    The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high ac- curacy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non- infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance pro- vides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread.
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    Data and Code for Mechanistic links between physiology and spectral reflectance enable pre-visual detection of oak wilt and drought stress
    (2024-01-04) Sapes, Gerard; Schroeder, Lucy; Scott, Allison; Clark, Isaiah; Juzwik, Jennifer; Montgomery, Rebecca; Guzman Q., J. Antonio; Cavender-Bares, Jeannine; gsapes@ufl.edu; Sapes, Gerard; University of Minnesota; University of Florida; Northern Research Station, USDA Forest Service
    Tree mortality due to global change-including range expansion of invasive pests and pathogens- is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen to detect and distinguish both types of stressors. We also performed a field experiment where we validated the capacity of spectral reflectance models to predict physiological status and distinguish oak wilt from healthy trees. The data and code provided here address these goals.
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    Leaf and canopy spectra, symptom progression, and physiological data from experimental detection of oak wilt in oak seedlings
    (2019-04-26) Fallon, Beth; Yang, Anna; Nguyen, Cathleen; Armour, Isabella; Juzwik, Jennifer; Montgomery, Rebecca A.; Cavender-Bares, Jeannine; eafallon@gmail.com; Fallon, Beth; University of Minnesota, Department of Ecology, Evolution, and Behavior; University of Minnesota, Department of Forestry; US Forest Service Northern Research Station
    These data were collected as part of an experimental effort to accurately detect oak wilt infections in oak seedlings using remote sensing tools and to differentiate that disease stress from other mechanisms of tree decline. Oak wilt disease causes rapid mortality in oaks in the central and eastern United States. Management of the disease requires early diagnosis and tree removal to prevent fungal spread. Hyperspectral tools provide a potential method of early remote diagnosis, but accurately differentiating oak wilt from other agents of oak decline is integral to effective management. We conducted experiments (2017 and 2018) on two year old seedlings of Quercus ellipsoidalis and Q. macrocarpa in which treatments were 1) maintained as healthy individuals, 2) subjected to chronic drought, or inoculated 3) stems with oak wilt fungus (Bretziella fagacearum, a fungal vascular wilt) or 4) leaves with bur oak blight fungus (Tubakia iowensis, a fungal leaf pathogen). We measured leaf and whole plant hyperspectral reflectance (350 to 2400nm, Spectra Vista HR 1024i spectroradiometer (Spectra Vista Corporation, New York, USA)), gas exchange (LI-6440XT with a leaf chamber fluorometer attachment (LI-COR Environmental, Nebraska, USA)), and tracked symptom development in repeated measures of seedlings over the course of each experiment. In 2018, we explicitly measured spectral reflectance and gas exchange on both symptomatic and green leaves, as available and we also measured collected thermal images of leaves twice during the experiment (2018 only).
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    Mapping oak wilt disease from space using land surface phenology
    (Remote Sensing of Environment, 2023-12-01) Guzmán, Jose A; Pinto-Ledezma, Jesús N; Frantz, David; Townsend, Philip A; Juzwik, Jennifer; Cavender-Bares, Jeannine
    Protecting the future of forests relies on our ability to observe changes in forest health. Thus, developing tools for sensing diseases in a timely fashion is critical for managing threats at broad scales. Oak wilt —a disease caused by a pathogenic fungus (Bretziella fagacearum)— is threatening oaks, killing thousands yearly while negatively impacting the ecosystem services they provide. Here we propose a novel workflow for mapping oak wilt by targeting temporal disease progression through symptoms using land surface phenology (LSP) from spaceborne observations. By doing so, we hypothesize that phenological changes in pigments and photosynthetic activity of trees affected by oak wilt can be tracked using LSP metrics derived from the Chlorophyll/Carotenoid Index (CCI). We used dense time-series observations from Sentinel-2 to create Analysis Ready Data across Minnesota and Wisconsin and to derive three LSP metrics: the value of CCI at the start and end of the growing season, and the coefficient of variation of the CCI during the growing season. We integrate high-resolution airborne imagery in multiple locations to select pixels (n = 3872) from the most common oak tree health conditions: healthy, symptomatic for oak wilt, and dead. These pixels were used to train an iterative Partial Least Square Discriminant (PLSD) model and derive the probability of an oak tree (i.e., pixel) in one of these conditions and the associated uncertainty. We assessed these models spatially and temporally on testing datasets revealing that it is feasible to discriminate among the three health conditions with overall accuracy between 80 and 82%. Within conditions, our models suggest that spatial variations among three CCI-derived LSP metrics can identify healthy (Area Under the Curve (AUC) = 0.98), symptomatic (AUC = 0.89), and dead (AUC = 0.94) oak trees with low false positive rates. The model performance was robust across different years as well. The predictive maps were used to guide local stakeholders to locate disease hotspots for ground verification and subsequent decision-making for treatment. Our results highlight the capabilities of LSP metrics from dense spaceborne observations to map diseases and to monitor large-scale change in biodiversity.
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    Mechanistic links between physiology and spectral reflectance enable pre-visual detection of oak wilt and drought stress
    (Proceedings of the National Academy of Sciences, 2024-02) Sapes, Gerard; Schroeder, Lucy; Scott, Allison; Clark, Isaiah; Juzwik, Jennifer; Montgomery, Rebecca; Guzmán Q., J. Antonio; Cavender-Bares, Jeannine
    Tree mortality due to global change—including range expansion of invasive pests and pathogens—is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen. Models developed from spectral reflectance accurately predicted ecophysiological indicators of oak wilt and drought decline in both potted and field experiments with naturally grown saplings. Both oak wilt and drought resulted in blocked water transport through xylem conduits. However, oak wilt impaired conduits in localized regions of the xylem due to formation of tyloses instead of emboli. The localized tylose formation resulted in more variable canopy photosynthesis and water content in diseased trees than drought-stressed ones. Reflectance signatures of plant photosynthesis, water content and cellular damage detected oak wilt and drought 13 days before visual symptoms appeared. Our results show that leaf spectral reflectance models predict ecophysiological processes relevant to detection and differentiation of disease and drought. Coupling spectral models that detect physiological change with spatial information enhances capacity to differentiate plant stress types such as oak wilt and drought.
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    Oak Wilt in Minnesota
    (St. Paul, MN: University of Minnesota Extension Service, 1999) French, David W.; Juzwik, Jennifer