Baldissara Megan, Barg Allison, Little Andrew, Tang Zhenghong, Wardlow Brian, Uden Daniel R
Applied Wildlife Ecology and Spatial Movement Lab, School of Natural Resources University of Nebraska-Lincoln Lincoln Nebraska USA.
Community and Regional Planning Program University of Nebraska-Lincoln Lincoln Nebraska USA.
Ecol Evol. 2025 Jun 21;15(6):e71576. doi: 10.1002/ece3.71576. eCollection 2025 Jun.
Documenting wildlife-habitat relationships at multiple scales is essential for conservation. Remote sensing datasets and their derivatives (e.g., landcover data) enable efficient multi-scale assessment of ring-necked pheasant () habitat, albeit with trade-offs among their thematic, spatial, temporal, and/or spectral grains and extents. For example, the National Agriculture Imagery Program provides fine spatial but coarse spectral grain imagery, both important for identifying pheasant habitats. Spatial technologies and datasets relevant to pheasant research are advancing, yet the information on the data sources utilized in research to date is limited. Remote sensing and landcover datasets surveys in pheasant research could help fill information gaps in pheasant-habitat relationships. In this systematic review, we filtered 1110 peer-reviewed pheasant habitat studies to 65 from the Central United States. Temporal trends were tested in the broad use of remote sensing and the selection of remote sensing platforms and data types. Of the selected studies, 26 used remote sensing or landcover data, which were classified by the thematic, spatial, temporal, and spectral grains and extents. Remote sensing and landcover data products increased over time, particularly satellite-based landcover products with relatively coarse thematic resolutions (e.g., crops and grassland), moderate spatial grains (e.g., 30-m), and spatial extents (e.g., smaller than the average US county). Remote sensing photography/imagery with multispectral sensors and coarse spectral resolution (e.g., three bands with 100 nm width) was also prominent but remained constant over time. We found no evidence of research with remote sensing or landcover data at multiple temporal grains and extents. Several studies lacked scale reporting, potentially limiting our inference. Scale transparency is important due to species selecting their habitat at multiple scales, making findings scale-dependent. Effective conservation requires scale-appropriate strategies. As remote sensing advances, opportunities for ring-necked pheasant habitat multi-scale assessment that fill remaining pheasant-habitat relationships knowledge gaps and support management decisions will increase.
记录多尺度下的野生动物 - 栖息地关系对于保护工作至关重要。遥感数据集及其衍生数据(如土地覆盖数据)能够对环颈雉()栖息地进行高效的多尺度评估,不过在其主题、空间、时间和/或光谱粒度及范围之间存在权衡。例如,国家农业影像计划提供了空间分辨率高但光谱粒度粗的影像,这两者对于识别雉鸡栖息地都很重要。与雉鸡研究相关的空间技术和数据集不断发展,但迄今为止关于研究中使用的数据源的信息有限。在雉鸡研究中进行遥感和土地覆盖数据集调查有助于填补雉鸡 - 栖息地关系方面的信息空白。在这项系统综述中,我们将1110篇同行评议的雉鸡栖息地研究筛选至来自美国中部的65篇。对遥感的广泛应用以及遥感平台和数据类型的选择进行了时间趋势测试。在所选研究中,26篇使用了遥感或土地覆盖数据,这些数据按主题、空间、时间和光谱粒度及范围进行了分类。遥感和土地覆盖数据产品随时间增加,特别是基于卫星的土地覆盖产品,其主题分辨率相对较粗(如作物和草地)、空间粒度适中(如30米)且空间范围(如小于美国平均县)。具有多光谱传感器和粗光谱分辨率(如三个带宽为100纳米的波段)的遥感摄影/影像也很突出,但随时间保持不变。我们没有发现关于在多个时间粒度和范围使用遥感或土地覆盖数据进行研究的证据。一些研究缺乏尺度报告,这可能会限制我们的推断。尺度透明度很重要,因为物种在多个尺度上选择其栖息地,这使得研究结果依赖于尺度。有效的保护需要适合尺度的策略。随着遥感技术的进步,填补剩余雉鸡 - 栖息地关系知识空白并支持管理决策的环颈雉栖息地多尺度评估机会将会增加。