Efficient wildlife monitoring methods are necessary for biodiversity conservation and management. The combination of remote sensing, aerial imagery and deep learning offer promising opportunities to renew or improve existing survey methods. The complementary use of visible (VIS) and thermal infrared (TIR) imagery can add information compared to a single-source image and improve results in an automated detection context. However, the alignment and fusion process can be challenging, especially since visible and thermal images usually have different fields of view (FOV) and spatial resolutions. This research presents a case study on the great blue heron (Ardea herodias) to evaluate the performances of synchronous aerial VIS and TIR imagery to automatically detect individuals and nests using a YOLO11n model. Two VIS-TIR fusion methods were tested and compared: an early fusion approach and a late fusion approach, to determine if the addition of the TIR image gives any added value compared to a VIS-only model. VIS and TIR images were automatically aligned using a deep learning model. A principal component analysis fusion method was applied to VIS-TIR image pairs to form the early fusion dataset. A classification and regression tree was used to process the late fusion dataset, based on the detection from the VIS-only and TIR-only trained models. Across all classes, both late and early fusion improved the F1 score compared to the VIS-only model. For the main class, occupied nest, the late fusion improved the F1 score from 90.2 (VIS-only) to 93.0%. This model was also able to identify false positives from both sources with 90% recall. Although fusion methods seem to give better results, this approach comes with a limiting TIR FOV and alignment constraints that eliminate data. Using an aircraft-mounted very high-resolution visible sensor could be an interesting option for operationalizing surveys.

翻译：高效的野生动物监测方法对于生物多样性保护与管理至关重要。遥感技术、航空影像与深度学习的结合为更新或改进现有调查方法提供了广阔前景。可见光（VIS）与热红外（TIR）影像的互补使用相较于单源影像能提供更多信息，并在自动化检测场景中提升效果。然而，影像配准与融合过程存在挑战，特别是可见光与热红外图像通常具有不同的视场角（FOV）和空间分辨率。本研究以大蓝鹭（Ardea herodias）为案例，通过YOLO11n模型评估同步航空可见光与热红外影像在自动识别个体与鸟巢方面的性能。测试并比较了两种VIS-TIR融合方法：早期融合与晚期融合，以探究相较于仅使用可见光模型，热红外图像的加入是否带来附加价值。VIS与TIR影像通过深度学习模型实现自动配准。对VIS-TIR图像对采用主成分分析融合方法构建早期融合数据集；晚期融合数据集则基于仅VIS与仅TIR训练模型的检测结果，通过分类回归树进行处理。在所有类别中，晚期与早期融合均较仅VIS模型提升了F1分数。针对主要类别“占用巢穴”，晚期融合将F1分数从90.2%（仅VIS）提高至93.0%。该模型还能以90%的召回率识别两类源数据中的误检目标。尽管融合方法展现出更优结果，但该方案受限于热红外视场角与配准约束导致部分数据丢失。采用机载超高分辨率可见光传感器或可为调查业务化实施提供可行路径。