Digital phytocommunity mapping using UAV-based hyperspectral imaging for plant health assessment

Thesis Abstract

Digital phytocommunity mapping using UAV-based hyperspectral imaging for plant health assessment seeks to advance the spatially explicit understanding of plant health dynamics across diverse agroecosystems through an integrative sensing and data analytics framework. The study addresses the problem of limited, scalable, and objective monitoring of plant health at canopy and community levels, which constrains timely intervention and sustainable management. The aim is to develop a robust workflow that combines unmanned aerial vehicle (UAV)–acquired hyperspectral imagery with machine learning and ecological theory to map phytocommunity health status, detect stress signatures, and quantify health-driven compositional changes over time. Specific objectives include (1) to quantify spectral reflectance signatures across representative plant communities under abiotic and biotic stress gradients using 5–8 nm hyperspectral resolution data; (2) to develop and validate a predictive model linking spectral indices and local environmental variables to health metrics such as chlorophyll content, leaf area index, and photosynthetic capacity; (3) to generate high-resolution health maps at 1–2 m spatial resolution and assess their accuracy against ground-truth measures; (4) to analyze temporal dynamics of phytocommunity health over two growing seasons and identify early warning indicators of deteriorating health; and (5) to provide a decision-support framework integrating the health maps with management actions for precision agriculture and conservation planning. The methodology employs a sequential explanatory mixed-methods design. The population comprises diverse phytocommunity assemblages in temperate agricultural landscapes, including cereal, legume, and weed-dominated plots (n ? 60). A stratified random sample yields 180 UAV flights across three sites, with ground-truth sampling including 2,400 foliar measurements (chlorophyll a+b, carotenoids, nitrogen status) and 120 phenological assessments. Data collection instruments include a lightweight multi-sensor UAV platform equipped with a visible–near infrared (VNIR) to shortwave infrared (SWIR) hyperspectral imager (350–2500 nm), spectroradiometers for in-situ leaf measurements, and environmental sensors for microclimate data. Ground truth for health indicators comprises SPAD chlorophyll meter readings, normalized difference vegetation index (NDVI) derived from proximate sensors, and leaf area index (LAI) estimated via optical methods. Spectral data pre-processing follows standard radiometric calibration, atmospheric correction (MODTRAN-based), and geometric co-registration with field plots. Analytical procedures integrate spectral feature extraction and advanced modeling. Feature selection uses recursive feature elimination and partial least squares regression to identify informative bands and indices (e.g., PRI, MDVI, EVI2) linked to physiological stress markers. Predictive modeling combines random forest regression, support vector regression, and gradient boosting to estimate chlorophyll content, LAI, and photosynthetic capacity from spectral features and environmental covariates. The study also implements a hierarchical Bayesian framework to incorporate uncertainty in predictions and to fuse temporal data for change detection. Validation employs a nested cross-validation scheme, with performance metrics including R2, RMSE, and MAE. A conceptual model derived from stress physiology and community ecology theories (Liebig’s law of the minimum and the stress-gradient hypothesis) guides interpretation of spectral-health relationships and spatial patterns. Key expected findings include (i) a set of robust hyperspectral indicators that predict leaf- and canopy-level health metrics with R2 > 0.75 and RMSE within acceptable agronomic tolerances; (ii) high-fidelity health maps at 1–2 m resolution, achieving map accuracies exceeding 85% when validated against ground truth; (iii) detectable temporal signals of early stress before visible phenotypic symptoms, enabling proactive management; and (iv) quantified relationships between environmental stressors and phytocommunity health trajectories, informing adaptive management strategies. The study contributes to knowledge by integrating UAV-based hyperspectral imaging with ecological theory to operationalize phytocommunity health assessment, offering a scalable, data-driven framework for precision agriculture and ecosystem monitoring. It provides a replicable workflow, validated models, and decision-support tools that link remote sensing signals to actionable health indicators. The main conclusion posits that UAV-derived hyperspectral data, coupled with robust predictive modeling, can reliably diagnose, map, and anticipate plant health dynamics across complex phytocommunities, thereby enhancing timely interventions and promoting sustainable management. Recommendations include expanding spatial and temporal scales, refining spectral libraries for diverse taxa, and integrating this framework with farm management information systems for real-time decision-making.