Evaluating the influence of biocontrol program on the Colorado River biodiversity with multi-source time series imagery

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Geography

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Zhao, Yilun

Contributors dc:contributor

• Diao, Chunyuan
• Wang, Shaowen
• Lara, Mark J.
• Wang, Jida

• Time Series
• Biocontrol
• Leaf Phenology
• Change Detection
• Plant Biodiversity

Statement dc:rights

• Copyright 2025 Yilun Zhao

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130084

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130084