使用Sentinel-1和Sentinel-2对高度特定湿地鸟类的空间显式丰度建模使用Sentinel-1和Sentinel-2对高度特定湿地鸟类的空间显式丰度建模

IF 2 4区 地球科学 Q3 REMOTE SENSING
L. McLeod, Evan R. DeLancey, Erin M. Bayne
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引用次数: 1

摘要

黄轨是一种高度特化的湿地专性鸟类。它们在加拿大是一个面临风险的物种,人们对它们在西部北方森林湿地的丰度知之甚少。新兴技术使我们能够通过使用地面遥感器(自主记录单元;ARU)进行被动声学监测,有效地调查黄铁和其他偏远地区的湿地鸟类。我们分析了阿尔伯塔省生物多样性监测研究所生物声学部门领导的持续监测项目前四年(2013-2016年)的鸟类数据。我们使用谷歌地球引擎处理的哨兵1号和哨兵2号的卫星数据开发了物种丰度模型。我们从合成孔径雷达和光学遥感中确定了对这种湿地鸟类具有很强预测能力的协变量(AUC=0.96)。预计阿尔伯塔省东北部研究区约1.5%的可用湿地栖息地非常适合黄铁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatially Explicit Abundance Modeling of a Highly Specialized Wetland Bird Using Sentinel-1 and Sentinel-2 Modélisation spatialement explicite de l’abondance d’un oiseau très spécifique aux zones humides à l’aide de Sentinel-1 et de Sentinel-2
Abstract Yellow Rail (Coturnicops noveboracensis) are a highly specialized wetland obligate bird. They are a species at risk in Canada and very little is known about their abundance in the wetlands of the western boreal forest. Emerging technologies have enabled us to effectively survey for Yellow Rail and other wetland birds in remote areas by using ground-based remote sensors (autonomous recording units; ARUs) to conduct passive acoustic monitoring. We analyzed bird data from the first four years (2013–2016) of an ongoing monitoring program led by the Bioacoustic Unit at the Alberta Biodiversity Monitoring Institute. We developed species abundance models using satellite data from Sentinel-1 and Sentinel-2 processed in Google Earth Engine. We identified covariates from both synthetic aperture radar and optical remote sensing that had strong predictive capacity for this wetland bird (AUC = 0.96). Approximately 1.5% of available wetland habitat in our northeast Alberta study area was predicted to be highly suitable for Yellow Rail.
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来源期刊
自引率
3.80%
发文量
40
期刊介绍: Canadian Journal of Remote Sensing / Journal canadien de télédétection is a publication of the Canadian Aeronautics and Space Institute (CASI) and the official journal of the Canadian Remote Sensing Society (CRSS-SCT). Canadian Journal of Remote Sensing provides a forum for the publication of scientific research and review articles. The journal publishes topics including sensor and algorithm development, image processing techniques and advances focused on a wide range of remote sensing applications including, but not restricted to; forestry and agriculture, ecology, hydrology and water resources, oceans and ice, geology, urban, atmosphere, and environmental science. Articles can cover local to global scales and can be directly relevant to the Canadian, or equally important, the international community. The international editorial board provides expertise in a wide range of remote sensing theory and applications.
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