遥感
物候学
环境科学
每年落叶的
比例(比率)
植被(病理学)
公民科学
卫星
卫星图像
地图学
计算机科学
地理
生态学
植物
医学
生物
工程类
病理
航空航天工程
作者
Eric Graham,Erin C. Riordan,Eric Yuen,Deborah Estrin,Philip W. Rundel
标识
DOI:10.1111/j.1365-2486.2010.02164.x
摘要
Abstract Plant phenology is highly sensitive to changes in environmental conditions and can vary widely across landscapes. Current observation methods are either manual for small‐scale, high precision measurements or by satellite remote sensing for large‐scale, low spatial resolution measurement. The development of inexpensive approaches is necessary to advance large scale, high precision phenology monitoring. The use of publicly available, Internet‐connected cameras, often associated with airports, national parks, and roadway conditions, for detecting and monitoring plant phenology at a continental scale can augment existing ground and satellite‐based methodologies. We collected twice‐daily images from over 1100 georeferenced public cameras across North America from February 2008 to 2009. Using a test subset of these cameras, we compared modeled spring ‘green‐up’ with that from co‐occurring remote sensing products. Although varying image exposure and color correction introduced noise to camera measurements, we were able to correlate spring green‐up across North America with visual validation from images and detect a latitudinal trend. Public cameras had an equivalent or higher ability to detect spring compared with satellite‐based data for corresponding locations, with fewer numbers of poor quality days, shorter continuous bad data days, and significantly lower errors of spring estimates. Manual image segmentation into deciduous, evergreen, and understory vegetation allowed detection of spring and fall onset for multiple vegetation types. Additional advantages of a public camera‐based monitoring system include frequent image capture (subdaily) and the potential to detect quantitative responses to environmental changes in organisms, species, and communities. Public cameras represent a relatively untapped and freely available resource for supporting large‐scale ecological and environmental monitoring.
科研通智能强力驱动
Strongly Powered by AbleSci AI