Semester of Graduation

Spring 2026

Degree

Master of Science (MS)

Department

Geography and Anthropology

Document Type

Thesis

Abstract

Climate change-driven bark beetle outbreaks pose severe threats to Norway spruce forests across Europe, yet early detection of infested trees remains difficult with conventional field surveys or satellite imagery. Existing detection methods, such as traditional and deep learning-based methods, require extensive time, training data, and computational resources while underutilizing dense temporal observations. We present an integrated UAV framework that merges deep learning-based tree detection with multitemporal spectral analysis for early, tree-level identification of bark beetle stress. A key novelty of this research is the use of high-frequency UAV time series to enable temporal change-detection methods such as CUSUM, providing scalable early-warning capabilities previously unattainable at individual-tree resolution.

Firstly, a novel Fused YOLO-SAM pipeline was developed to automatically detect and delineate Norway spruce trees using UAV imagery collected over the mixed forests. Three YOLO v12 models were trained separately on summer (June), fall (November), and combined seasonal datasets acquired with a DJI Phantom drone having 5-band camera. The fall-trained model achieved the highest detection performance (F1 = 0.827, mAP50 = 0.916) and demonstrated superior cross-seasonal generalization, producing accurate and transferable crown segmentations when integrated with the Segment Anything Model (SAM). These automatically delineated spruce canopies provided a consistent and scalable base for individual tree-level spectral analysis.

To discriminate between healthy and bark beetle-infested Norway spruce trees at the individual tree level, the delineated crowns were subsequently used to extract per-tree spectral time series for stress analysis. Radiometrically calibrated multispectral bands and vegetation indices were evaluated using cumulative sum (CUSUM)-based Receiver Operating Characteristic (ROC)-Area under Curve (AUC) and effect size metrics. The results demonstrated that calibration significantly improves early-season discrimination, with near-infrared and red-edge bands achieving stable performance (mean AUC ≈ 0.75-0.80; Cohen’s d ≈ 0.9-1.2). In contrast, vegetation indices such as NDRE and MSR-RE exhibited lower discrimination and greater temporal variability. This research also performed comparisons of calibrated multispectral features with uncalibrated RGB features. The analysis indicated that selected RGB indices (notably ExG and GCC) is comparable to the performance of the multispectral bands (REG, NIR), showing positive early discrimination and mean AUC values around 0.70-0.75 which can provide operational benefit for forest monitoring and management when the expensive multispectral cameras are unavailable. Overall, this research demonstrates a scalable and operationally feasible pipeline that integrates automated tree delineation with robust spectral stress diagnostics, supporting early bark beetle detection and informed forest management under resource and data constraints.

Date

3-23-2026

Committee Chair

Meng, Xuelian

LSU Acknowledgement

1

LSU Accessibility Acknowledgment

1

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Available for download on Thursday, March 22, 2029

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