Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

Gerard Sapes, Cathleen Lapadat, Anna K Schweiger, Jennifer Juzwik, Rebecca Montgomery, Hamed Gholizadeh, Philip A. Townsend, John A. Gamon, Jeannine Cavender-Bares

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 accuracy 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 provides 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.

Original languageEnglish (US)
Article number112961
JournalRemote Sensing of Environment
Volume273
DOIs
StatePublished - May 2022

Bibliographical note

Funding Information:
This project was funded by the Minnesota Invasive Terrestrial Plants and Pests Center, the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA) through the Dimensions of Biodiversity program (DEB-1342872 and DEB-1342778), the Cedar Creek Long Term Ecological Research program (1831944), and the NSF Biology Integration Institute ASCEND (DBI: 2021898). The University of Minnesota, including Cedar Creek ESR, lies on the ancestral, traditional, and contemporary Land of the Dakota people. We would like to thank Brett Fredericksen, Erin Murdock, Kali Hall, Lewis French and Travis Cobb for help with tarp measurement for empirical line corrections. Thanks to Dr. Joe Knight for access to the infrastructure of the Remote Sensing and Geospatial Analysis Lab at UMN, and to Dan Bahauddin for informatics help which facilitated flight planning at Cedar Creek. We thank Paul Castillo, USFS for his technical assistance in the training of field technicians, and Zhihui Wang for assistance processing AVIRIS-NG imagery. We also thank Shan Kothari, Lucy Shroeder, Anna Yang, Austin Yantes, Clarissa Fontes, Byju Govidan, Artur Stefanski, and Adriana Castillo Castillo and three anonymous reviewers for their comments in previous versions of this manuscript.

Funding Information:
This project was funded by the Minnesota Invasive Terrestrial Plants and Pests Center , the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA) through the Dimensions of Biodiversity program ( DEB-1342872 and DEB-1342778 ), the Cedar Creek Long Term Ecological Research program ( 1831944 ), and the NSF Biology Integration Institute ASCEND (DBI: 2021898 ). The University of Minnesota, including Cedar Creek ESR, lies on the ancestral, traditional, and contemporary Land of the Dakota people. We would like to thank Brett Fredericksen, Erin Murdock, Kali Hall, Lewis French and Travis Cobb for help with tarp measurement for empirical line corrections. Thanks to Dr. Joe Knight for access to the infrastructure of the Remote Sensing and Geospatial Analysis Lab at UMN, and to Dan Bahauddin for informatics help which facilitated flight planning at Cedar Creek. We thank Paul Castillo, USFS for his technical assistance in the training of field technicians, and Zhihui Wang for assistance processing AVIRIS-NG imagery. We also thank Shan Kothari, Lucy Shroeder, Anna Yang, Austin Yantes, Clarissa Fontes, Byju Govidan, Artur Stefanski, and Adriana Castillo Castillo and three anonymous reviewers for their comments in previous versions of this manuscript.

Publisher Copyright:
© 2022 Elsevier Inc.

Keywords

  • Disease response
  • Oak wilt
  • Photosynthetic decline
  • Phylogenetic classification
  • Physiology
  • Remote sensing
  • Spectral reflectance
  • Water content

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