Evaluating the influence of biocontrol program on the Colorado River biodiversity with multi-source time series imagery
Zhao, Yilun
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/130084
Description
Title
Evaluating the influence of biocontrol program on the Colorado River biodiversity with multi-source time series imagery
Author(s)
Zhao, Yilun
Issue Date
2025-07-09
Director of Research (if dissertation) or Advisor (if thesis)
Diao, Chunyuan
Doctoral Committee Chair(s)
Diao, Chunyuan
Committee Member(s)
Wang, Shaowen
Lara, Mark J.
Wang, Jida
Department of Study
Geography & GIS
Discipline
Geography
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Time series
Biocontrol
Leaf phenology
Change detection
Plant biodiversity
Abstract
Biological control is a widely adopted strategy for managing invasive plant species through the release of natural enemies. While effective in reducing the abundance of target species, the broader ecological consequences, particularly for plant biodiversity, remain poorly understood. This dissertation proposes a remote sensing framework to evaluate changes in plant biodiversity following biocontrol interventions, using the case of saltcedar (Tamarix spp.) management in the southwestern United States.
High-resolution PlanetScope imagery was calibrated using fused MODIS and Harmonized Landsat Sentinel-2 (HLS) data to extract spring leaf phenological metrics at the individual tree level. The calibrated time series enabled the detection of budburst and leaf expansion, which proved effective for distinguishing species based on their unique seasonal growth patterns. Phenological traits derived from this approach provided valuable insights into species differentiation and functional diversity estimation.
To identify biocontrol-induced changes in vegetation, a Bidirectional Self-Attention Long Short-Term Memory (BiSA-LSTM) based change detection system was applied to Landsat time series. This model captured subtle and gradual changes in land cover associated with defoliation and vegetation regrowth, highlighting the complex and often prolonged ecological responses to biocontrol agents.
Spectral diversity was analyzed using a contrastive autoencoder-based model that clustered latent representations of phenological spectral signatures. This unsupervised method reduced the need for extensive field data and revealed increases in spectral heterogeneity following vegetation recovery, potentially indicating improved biodiversity conditions after biocontrol.
By combining phenological analysis, change detection, and spectral diversity estimation, this research advances remote sensing applications for ecological monitoring. The proposed framework offers scalable methods for detecting biodiversity responses across space and time and provides practical tools to support conservation efforts, guide biocontrol management, and inform restoration strategies in dynamic and data-limited environments.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
