Effective management of rare species requires an understanding of spatial variation in abundance, which is challenging to estimate. We tested the efficacy of high-resolution imagery to detect burrows of the Texas kangaroo rat (TKR; Dipodomys elator) as a means of estimating abundance across its geographic range. Specifically, we estimated burrow counts using an Unmanned Aerial System (UAS) to collect data from very high-resolution Red–Green–Blue (RGB) imagery and estimate digital elevation (2.5-mm pixel resolution) over active and inactive burrows located on mesquite mounds and anthropogenic features (roadsides, fences, etc.). In 2018, we identified 26 burrow locations on a private ranch in Wichita County, Texas, USA, and characterized burrows based on topography and vegetation density. We found that TKR burrows can only be identified with data of <5 cm pixel resolution, thus eliminating the possibility of using high-resolution imagery data currently available for Texas. Alternatively, we propose that the use of National Agriculture Imagery Program (NAIP) imagery at 0.5- and 0.6-m pixel resolution, in combination with resampled digital elevation data, can provide an effective means for identifying potential TKR burrow locations at the county level. We present 3 different approaches at the county and local scale that combine topographic and vegetation fractional cover information using a weighted overlay approach. The modeling approaches have strong predictive capabilities and can be integrated with UAS data for visual confirmation of active and inactive burrows. We concluded that very high-resolution imagery and topographic information at pixel resolutions <5 cm collected by airborne systems can effectively help locate active TKR burrows. However, to remain cost effective, upscaling to the county level will require reducing the sampling area to the most suitable habitat. Modeling approaches, such as those proposed in this study, can help effectively locate these sampling areas.

中文翻译：

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遥感技术对稀有袋鼠洞穴数量估计的有效性

对稀有物种的有效管理需要了解丰度的空间变化，而这很难估计。我们测试了高分辨率图像检测德克萨斯袋鼠（TKR；Dipodomys elator）洞穴的功效，作为估计其地理范围内丰度的一种手段。具体来说，我们使用无人机系统（UAS）从非常高分辨率的红绿蓝（RGB）图像中收集数据来估计洞穴数量，并估计位于豆科灌木上的活动和非活动洞穴的数字高程（2.5毫米像素分辨率）土丘和人为特征（路边、栅栏等）。2018 年，我们在美国德克萨斯州威奇托县的一个私人牧场上确定了 26 个洞穴位置，并根据地形和植被密度对洞穴进行了特征描述。我们发现 TKR 洞穴只能使用 <5 厘米像素分辨率的数据进行识别，从而消除了使用德克萨斯州目前可用的高分辨率图像数据的可能性。另外，我们建议使用 0.5 米和 0.6 米像素分辨率的国家农业影像计划 (NAIP) 影像，结合重新采样的数字高程数据，可以为识别县级潜在的 TKR 洞穴位置提供有效的方法。我们在县和地方尺度上提出了 3 种不同的方法，使用加权叠加方法将地形和植被覆盖率信息结合起来。该建模方法具有强大的预测能力，可以与无人机数据集成，以目视确认活动和非活动的洞穴。我们得出的结论是，机载系统收集的像素分辨率<5厘米的超高分辨率图像和地形信息可以有效帮助定位活动的TKR洞穴。然而，为了保持成本效益，升级到县一级将需要将采样区域缩小到最合适的栖息地。建模方法（例如本研究中提出的方法）可以帮助有效地定位这些采样区域。