Datasets_UAV_Early_Warning

Datasets created within the study "Early detection of bark beetles by drone images differs in endemic and epidemic populations". The aim of the research is to compare the appearance and the detectability of early symptoms in endemic and epidemic bark beetle populations using high-resolution UAV multispectral imagery. Results show that high population density triggers a more rapid and intense response regarding the emergence of symptoms, allowing an earlier detection of the infestation.The datasets include: the median values of the vegetational indices calculated for every identified tree; the tree condition (infested, healthy or other), the survey dates, the experimental site identity, the trees identity (as described in the Materials and Methods section of the study).

本数据集源自题为《基于无人机影像的小蠹虫早期检测：地方性种群与暴发种群存在差异》的研究。该研究的核心目标为：借助高分辨率无人机（Unmanned Aerial Vehicle, UAV）多光谱影像，对比地方性与暴发种群小蠹虫的早期症状特征及其可检测性。研究结果表明，较高的种群密度会促使症状出现的过程更为快速且强烈，从而实现虫害侵染的更早检测。本数据集涵盖以下内容：1. 每株已识别树木的植被指数中值；2. 树木健康状态（受侵染、健康或其他）；3. 调查日期；4. 试验地块标识；5. 树木标识（详细说明参见该研究的《材料与方法》章节）。