Most small rodent populations worldwide exhibit fascinating population dynamics, capturing the attention of numerous scholars due to their multiyear cyclic fluctuations in population size and the astonishing amplitude of these fluctuations. Hulunbuir steppe stands as a crucial global hub for livestock production, yet in recent decades, the area has faced recurring challenges from steppes rodent invasions, with Brandt’s vole (Lasiopodomys brandtii, BV) being particularly rampant among them. They not only exhibit seasonal reproduction but also strong social behavior, and are generally considered pests, especially during population outbreak years. Prior studies suggest that BV population outbreaks tend to occur across a wider geographic area, and a strong indicator for identifying rodent outbreaks is recognizing their burrow clusters (burrow systems). Hence, this paper conducts target object detection of BV burrow clusters in the typical steppes of Hulunbuir using two GF-2 satellite images from 2021 (the year of the BV outbreak). This task is accomplished by incorporating the Faster R-CNN model in combination with three detection approaches: object-based image classification (OBIC), based on vegetation index classification (BVIC), and based on texture classification (BTC). The results indicate that OBIC demonstrated the highest robustness in BV burrow cluster detection, achieving an average AP of 63.80% and an F1 score of 0.722 across the two images. BTC exhibited the second-highest level of accuracy, achieving an average AP of 55.95% and an F1 score of 0.6660. Moreover, this approach displayed a strong performance in BV burrow clusters localization. In contrast, BVIC achieved the lowest level of accuracy among the three methods, with an average AP of only 29.45% and an F1 score of 0.4370. Overall, this study demonstrates the crucial role of utilizing high-resolution satellite imagery combined with DL-based object detection techniques in effectively monitoring and managing the potential outbreaks of steppe rodent pests across larger spatial extents.

中文翻译：

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利用 GF-2 卫星图像和更快的 R-CNN 模型对布兰特田鼠洞穴群进行检测和分类

全球大多数小型啮齿动物种群表现出令人着迷的种群动态，因其种群规模的多年周期性波动以及这些波动的惊人幅度而引起了众多学者的关注。呼伦贝尔草原是全球重要的畜牧业生产中心，但近几十年来，该地区不断面临草原啮齿动物入侵的挑战，其中包括勃兰特田鼠。布氏毛鼠，BV）其中尤为猖獗。它们不仅表现出季节性繁殖，而且具有强烈的社会行为，通常被认为是害虫，特别是在人口爆发的年份。先前的研究表明，细菌性阴道病种群暴发往往发生在更广泛的地理区域，识别啮齿动物暴发的一个强有力的指标是识别它们的洞穴群（洞穴系统）。为此，本文利用2021年（BV爆发年份）的两幅高分二号卫星影像，对呼伦贝尔典型草原BV洞穴群进行目标物检测。该任务是通过将 Faster R-CNN 模型与三种检测方法相结合来完成的：基于对象的图像分类 (OBIC)、基于植被指数分类 (BVIC) 和基于纹理分类 (BTC)。结果表明，OBIC 在 BV 洞穴簇检测中表现出最高的鲁棒性，两幅图像的平均 AP 为 63.80%，F1 得分为 0.722。BTC 表现出第二高的准确率，平均 AP 为 55.95%，F1 得分为 0.6660。此外，该方法在 BV 洞穴簇定位中表现出强大的性能。相比之下，BVIC 在三种方法中取得了最低的准确率，平均 AP 仅为 29.45%，F1 分数为 0.4370。总体而言，这项研究证明了利用高分辨率卫星图像与基于深度学习的目标检测技术相结合，在有效监测和管理更大空间范围内草原啮齿动物害虫潜在爆发方面的关键作用。