Accurate assessment of woody species diversity using remote sensing can assist ecologists by providing timely information for ecosystem management. The increasing availability of remotely sensed data necessitates the investigation of accuracies of different sensors in classifying plant species, especially during the dry season when foliage amount is low. WorldView-2, SPOT-6, and Sentinel-2 images were compared in detecting woody species (n = 27) and three coexisting land cover types in a savanna environment during a dry period. Random Forest (RF) and Support Vector Machine (SVM) classifiers were applied to each imagery to make a strong case for the comparison. The overall classification accuracies ranged between 52% and 65% for all images, with the WorldView-2 image performing the best followed by Sentinel-2 and SPOT-6 images. These accuracy rankings were similar for both the RF and SVM classifiers, with the former faring better. Pairwise comparison of the images using McNemar’s test showed significant differences between images in their ability to correctly identify woody species. Analysis of band importance revealed better contributions to the classifications of infrared bands for all images. Overall, the findings showed the potential of optical imagery in classifying and monitoring woody species hotspots in savanna environments even during a low photosynthesis season.

中文翻译：

---

调查 WorldView-2、Sentinel-2 和 SPOT-6 在旱季区分形态相似的稀树草原木本植物物种方面的准确性

使用遥感准确评估木本物种多样性可以通过为生态系统管理提供及时信息来帮助生态学家。随着遥感数据的日益普及，需要研究不同传感器在植物物种分类中的准确性，尤其是在树叶数量较少的旱季。比较 WorldView-2、SPOT-6 和 Sentinel-2 图像在干旱时期检测稀树草原环境中的木本物种 (n = 27) 和三种共存的土地覆盖类型。将随机森林 (RF) 和支持向量机 (SVM) 分类器应用于每个图像，以便为比较提供强有力的案例。所有图像的总体分类准确率介于 52% 和 65% 之间，WorldView-2 图像表现最好，其次是 Sentinel-2 和 SPOT-6 图像。RF 和 SVM 分类器的这些准确度排名相似，前者表现更好。使用 McNemar 检验对图像进行两两比较表明，图像在正确识别木本物种的能力方面存在显着差异。波段重要性分析揭示了对所有图像的红外波段分类的更好贡献。总体而言，研究结果表明，即使在光合作用较低的季节，光学图像在对稀树草原环境中的木本物种热点进行分类和监测方面也具有潜力。波段重要性分析揭示了对所有图像的红外波段分类的更好贡献。总体而言，研究结果表明，即使在光合作用较低的季节，光学图像在对稀树草原环境中的木本物种热点进行分类和监测方面也具有潜力。波段重要性分析揭示了对所有图像的红外波段分类的更好贡献。总体而言，研究结果表明，即使在光合作用较低的季节，光学图像在对稀树草原环境中的木本物种热点进行分类和监测方面也具有潜力。