Unmanned Aerial Systems (UAS) represent an important niche platform for measuring vegetation health, structure, and productivity; metrics that directly inform sustainable conservation and development initiatives in rural African savannas. Products derived from UAS imagery have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail, an advantage especially useful for challenging environments such as dryland savannas. This study uses UAS-captured imagery to assess the efficacy of UAS for monitoring structural characteristics of vegetation in a mixed savanna woodland. The main objective was to compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. We compare different sensor types to determine whether multispectral data improve estimates of vegetation structure at the expense of spatial resolution. Results indicate that leveraging multispectral reflectance information, particularly in the near-infrared portion of the spectrum, aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. We also compare two image segmentation techniques for crown delineation and found that all techniques perform best in grassy savanna sites where trees and shrubs are easily distinguishable. Overall, a region-growing technique consistently exhibits highest levels of agreement with in situ height and crown area measurements, while a simple height threshold is best for determining fractional coverage of structural classes present. Findings from this work contribute to the advancement for applying high spatial resolution, UAS-derived methods in remote sensing analyses with specific consideration towards autonomous crown delineation and resource management initiatives in dryland systems. Lastly, data-informed analyses, as presented here, provide robust scientific evidence that contribute to informing environmental management decisions when considering the use of UAS technology in conservation and wildlife management across Africa.

无人机系统（UAS）是衡量植被健康，结构和生产力的重要利基平台。可以直接为非洲农村大草原的可持续保护和发展计划提供依据的指标。与传统的卫星或飞机图像相比，来自UAS图像的产品具有更高的空间分辨率，从而可以更详细地绘制和监视植被的光谱和结构异质性，这一优势尤其适用于干旱地区的稀树草原。这项研究使用UAS捕获的图像来评估UAS监视热带稀树草原林地植被结构特征的功效。主要目的是比较多种从UAS影像中提取木本植被结构的方法，并评估之间的相关性。现场实地测量和UAS估计。我们比较不同的传感器类型，以确定多光谱数据是否以牺牲空间分辨率为代价来改善植被结构的估计。结果表明，利用多光谱反射率信息，尤其是在光谱的近红外部分，有助于冠状轮廓描绘，面积估计以及研究区域内木质和非木质植被的覆盖率。我们还比较了两种图像分割技术来进行树冠轮廓描绘，发现所有技术在草木稀树草原地区（树木和灌木很容易区分）表现最佳。总体而言，区域生长技术始终展现出最高的现场一致性高度和胎冠面积测量，而简单的高度阈值最适合确定存在的结构类别的分数覆盖率。这项工作的发现有助于在遥感分析中应用高空间分辨率，UAS衍生的方法，尤其是针对旱地系统中的自主树冠轮廓和资源管理计划。最后，如本文所述，以数据为依据的分析提供了有力的科学证据，当考虑在整个非洲的保护和野生动植物管理中使用UAS技术时，有助于为环境管理决策提供依据。