Contrasting Saltcedar Dynamics in Native and Non-Native Habitats through Integration of Remote Sensing and Population Modeling

通过遥感与种群建模的结合，对比本土和非本土栖息地的盐杉动态

• 批准号: 1951657
• 负责人: Chunyuan Diao
• 金额: $ 35.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951657&HistoricalAwards=false
• 关键词: Contrasting; Saltcedar; Dynamics; Native; Non

This project addresses the pressing problem of saltcedar invasion from a perspective that overcomes the spatial scale disparities between remote sensing and ground-based ecological studies. The overarching goal of this project is to develop an integrated remotely sensed population modeling framework to investigate the invasion mechanism based on the contrast of saltcedar dynamics in its native and non-native habitats. Through shedding light on the underlying mechanisms of saltcedar invasion across scales, the framework will contribute to the large-scale riparian restoration practices. Research findings will be broadly disseminated to conservation agencies to help predict and address the threat of invasive saltcedar. The synergistic educational and research activities will offer learning and research opportunities to students from secondary to graduate levels. Lastly, outreach activities will broaden the participation of traditionally underrepresented student communities in STEM related fields.Saltcedar invasion remains a severe ecological problem, negatively impacting riparian areas, with broad implications on society, the economy, and, ultimately, human health and wellbeing. Developing a comprehensive understanding of its spatial expansion and spread mechanisms is essential for proactive ecosystem management. The key research question of the project is: what are the contrasting dynamics of saltcedar in response to varying hydroclimatic factors across its native and non-native habitats? To answer this question, the investigators will develop an integrated remotely sensed population modeling multi-component framework. The integrated framework will form the basis for a contrasting saltcedar dynamic analysis across scales, and foster insights into the hydroclimatic regimes driving the vastly disparate dynamics of saltcedar across its native and non-native habitats. This multi-scalar framework will be transferable to other types of competing vegetation species, enhancing its utility widely, and contributing to more effective land management practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目从克服遥感和地面生态研究之间空间尺度差异的角度解决了盐雪松入侵的紧迫问题。该项目的总体目标是开发一个综合的遥感人口建模框架，调查入侵机制的基础上的对比盐杉动态在其本地和非本地栖息地。通过揭示盐杉入侵的潜在机制，该框架将有助于大规模的河岸恢复实践。研究结果将广泛传播给保护机构，以帮助预测和解决入侵盐柏的威胁。协同教育和研究活动将为中学至研究生水平的学生提供学习和研究机会。最后，外展活动将扩大传统上代表性不足的学生社区在STEM相关领域的参与。盐雪松入侵仍然是一个严重的生态问题，对河岸地区产生负面影响，对社会，经济，并最终对人类健康和福祉产生广泛影响。全面了解其空间扩展和传播机制对于积极主动的生态系统管理至关重要。该项目的关键研究问题是：盐雪松在其原生和非原生栖息地中对不同水文气候因素的响应对比动态是什么？为了回答这个问题，研究人员将开发一个集成的遥感人口建模多组件框架。综合框架将形成一个对比的盐雪松动态分析的基础上跨尺度，并促进深入了解水文气候制度驱动盐雪松在其本土和非本土栖息地的巨大差异的动态。这个多标量框架将转移到其他类型的竞争植被物种，提高其效用广泛，并有助于更有效的土地管理practices.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

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• DOI: 10.1080/01431161.2022.2145584
• 发表时间: 2022-09
• 期刊: International Journal of Remote Sensing
• 影响因子: 3.4
• 作者: Ying Lu;Le Wang

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

• DOI: 10.3390/rs13245005
• 发表时间: 2021-12
• 期刊: Remote. Sens.
• 作者: Zijun Yang;C. Diao;Bo Li

A comparative study on intra-annual classification of invasive saltcedar with Landsat 8 and Landsat 9

• DOI: 10.1080/01431161.2023.2195573
• 发表时间: 2023-03
• 期刊: International Journal of Remote Sensing
• 影响因子: 3.4
• 作者: Rui-Dong Li;Le Wang;Ying Lu

Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series

• DOI: 10.1016/j.rse.2023.113790
• 发表时间: 2023-11
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: Yilun Zhao;C. Diao;Carol K. Augspurger;Zi-Ling Yang

How to automate timely large-scale mangrove mapping with remote sensing

• DOI: 10.1016/j.rse.2021.112584
• 发表时间: 2021-10
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: Ying Lu;Le Wang