MSB-ECA: Leveraging NEON data to investigate remote sensing of biodiversity variables and scaling implications

MSB-ECA：利用 NEON 数据调查生物多样性变量的遥感和扩展影响

• 批准号: 1916896
• 负责人: Jessica Mitchell
• 金额: $ 23.75万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916896&HistoricalAwards=false
• 关键词: MSB; ECA; Leveraging; NEON; data

Periods in Earth's history can be divided into geological epochs that correspond to major changes in the evolution of life, such as the mass extinction of dinosaurs in the Cretaceous Period. A mass extinction event is currently underway that is many magnitudes greater than previous events recorded in the fossil record. Humanity is widely considered to be in a new period coined the Anthropocene Epoch on account of both extinction rates and the footprints our societies are recording on Earth?s surface. It is not surprising then that species numbers and biodiversity patterns are becoming increasingly relevant to human survival and a rising priority across the world. Biodiversity provides humanity with food, energy, and materials. The biodiversity of plant species can be measured on the ground using calculations such as the total number of species in an area, or the number of species weighted by the portion of area covered by each species. Global biodiversity trends can be collected from satellites but finer scale processes cannot be studied without an expanded network of ground and airborne observations of the same species over short-term intervals (every 1 to 5 years). Remotely sensed and ground based measurements from the National Ecological Observatory Network are well suited to complete this knowledge gap. This award will combine measurements collected simultaneously from the ground and the Network's Aerial Observation Platform, which has cutting edge optical imaging and three dimensional laser scanning systems onboard, to test the direct mapping of biodiversity variables. A method for measuring biodiversity will be demonstrated at eight Network sites located along a latitudinal gradient in eastern US temperate, broadleaf and mixed forest. Project activities will be conducted over two years at a Primarily Undergraduate Institution. Two workshops will be hosted that are designed to involve faculty and students across a network of Primarily Undergraduate Institutions through collaboration with Ecological Research as Education Network participants. Curriculum will be developed that focus on interdisciplinary training of students in field data analysis training, large dataset handling, macroecology theory and remote sensing. Value added remote sensing map products, educational material and the biodiversity processing framework generated from this project will be made available to National Ecological Observatory Network data users for testing other sites located in additional eco-climatic domains across the Continental US.Documenting and understanding patterns of biodiversity is a central issue in macroecology. To date, continental to global scale plant diversity mapping has been based on occurrence data, biased by uneven sampling and narrowly focused only on species richness. Vegetation diversity or phytodiversity occurs along a range of temporal, biological, and spatial scales, from genes to species, ecosystems and global biodiversity. Patches or corridors of biodiversity across a local landscape can reflect areas where fine scale patterns of species diversity are mechanistically related to broad scale dynamics as represented by environmental variables. The first component of this project explores an emerging concept in imaging spectroscopy where spatial patterns of reflectance correlate with species diversity (the spectral variation hypothesis). The second component of this project addresses cross-scale interactions and will directly benefit macrosystems biology by enhancing theory on scaling challenges related to spatial heterogeneity and interactions between fine scale species diversity and broader environmental variables across macroscale gradients such as latitude and time since glaciation. The project investigates the following three questions: 1) what are the spatial and spectral detection limits for species diversity mapping with Network Aerial Observation Platform datasets and by extension, how limited is simple species diversity upscaling, 2) what coarser scale natural and human variables are driving species diversity patterns at the site level and where are local diversity hotspots predicted to occur, and 3) how do patterns of and controls on species diversity change along macroscale gradients and how is spatial heterogeneity expected to influence macrosystem mapping and modeling? The value added optical and structural diversity map products that are generated under this award will be useful to larger remote sensing and ecology communities and help provide a basis for new and synoptic exploration of relationships between functional and taxonomic diversity in the US. The optical diversity products can also be used to run simulations that evaluate the role of future satellite-based spectroscopy missions in mapping Essential Biodiversity Variables. Overall processing flows developed through this project will create new methods for identifying potential landscape and genetic diversity hotspots and results can be used to test global biodiversity theories related to latitudinal and elevational distributions.

地球历史上的各个时期可以划分为与生命进化的重大变化相对应的地质时代，例如白垩纪恐龙的大规模灭绝。目前正在发生的一次大灭绝事件，比化石记录中记录的之前的事件大了许多。人类被广泛认为处于一个新的时期，被称为人类世时代，因为物种灭绝的速度和我们的社会在地球表面记录的足迹？S。因此，物种数量和生物多样性模式正变得与人类生存越来越相关，并成为世界各地日益重要的优先事项，这也就不足为奇了。生物多样性为人类提供食物、能源和材料。植物物种的生物多样性可以通过计算一个地区的物种总数或每一物种所覆盖的面积所加权的物种数量等方式在地面上进行测量。可以从卫星收集全球生物多样性趋势，但如果不在短期间隔内(每隔1至5年)扩大对同一物种的地面和空中观测网络，就无法研究更精细的尺度过程。来自国家生态观测站网络的遥感和地面测量非常适合填补这一知识鸿沟。该奖项将结合从地面和该网络的空中观测平台同时收集的测量结果，该平台上安装了尖端光学成像和三维激光扫描系统，以测试生物多样性变量的直接测绘。一种测量生物多样性的方法将在美国东部温带、阔叶林和混交林中沿纬度梯度的八个网络站点上进行演示。项目活动将在一个以本科生为主的机构进行，为期两年。将主办两个研讨会，旨在通过与生态研究作为教育网络参与者的合作，让主要由本科机构组成的网络中的教职员工和学生参加。将制定课程，重点是在实地数据分析培训、大数据集处理、宏观生态理论和遥感方面对学生进行跨学科培训。该项目产生的增值遥感地图产品、教育材料和生物多样性处理框架将提供给国家生态观测站和网络数据用户，用于测试位于美国大陆其他生态气候领域的其他地点。记录和了解生物多样性的模式是宏观生态学的一个中心问题。到目前为止，大陆到全球尺度的植物多样性测绘一直以发生数据为基础，由于抽样不均匀而存在偏见，仅狭隘地关注物种丰富度。植被多样性或植物多样性在一系列时间、生物和空间尺度上发生，从基因到物种、生态系统和全球生物多样性。当地景观上的生物多样性斑块或走廊可以反映出物种多样性的细微尺度格局与环境变量所代表的大尺度动态之间存在机械联系的地区。该项目的第一部分探索了成像光谱学中的一个新兴概念，其中反射率的空间模式与物种多样性相关(光谱变化假说)。该项目的第二个组成部分涉及跨尺度相互作用，并将通过加强与空间异质性有关的尺度挑战以及细尺度物种多样性与宏观尺度梯度(如纬度和冰川以来的时间)之间的更广泛环境变量之间的相互作用的理论，直接使大系统生物学受益。该项目调查了以下三个问题：1)利用网络航空观测平台数据集绘制物种多样性图的空间和光谱检测界限是什么，进而扩展到简单的物种多样性扩展有多大限度；2)什么较粗尺度的自然和人类变量正在推动立地一级的物种多样性格局，预计局部多样性热点将出现在哪里；以及3)物种多样性的格局和控制如何沿宏观尺度梯度变化，以及空间异质性预计将如何影响大系统制图和建模？根据该奖项产生的增值光学和结构多样性地图产品将对更大的遥感和生态社区有用，并有助于为美国功能多样性和分类多样性之间的关系提供新的和概括性的探索基础。光学多样性产品还可以用于运行模拟，以评估未来基于卫星的光谱任务在绘制基本生物多样性变量图中的作用。通过该项目开发的总体处理流程将为确定潜在的地貌和遗传多样性热点创造新的方法，结果可用于检验与纬度和海拔分布有关的全球生物多样性理论。

项目成果

Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

• DOI: 10.1002/ecs2.3992
• 发表时间: 2022-03
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: M. Farella;M. Barnes;D. Breshears;Jessica J. Mitchell;W. V. van Leeuwen;R. Gallery

Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community

• DOI: 10.1002/ecs2.3833
• 发表时间: 2021-12
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: R. C. Nagy;J. Balch;E. Bissell;M. Cattau;N. Glenn;B. Halpern;N. Ilangakoon;Brian Johnson;Maxwell B. Joseph;S. Marconi;Catherine O’Riordan;J. Sanovia;T. Swetnam;W. Travis;L. wasser;Elizabeth Woolner;P. Zarnetske;M. Abdulrahim;J. Adler;G. Barnes;K. Bartowitz;Rachael E. Blake;Sara P. Bombaci;J. Brun;Jacob D. Buchanan;K. Chadwick;Melissa S. Chapman;Steven S. Chong;Y. A. Chung;Jessica R. Corman;Jannelle Couret;E. Crispo;T. Doak;Alison Donnelly;Katharyn A. Duffy;K. Dunning;S. Durán;J. Edmonds;D. Fairbanks;A. Felton;C. Florian;D. Gann;M. Gebhardt;N. Gill;W. Gram;Jessica S. Guo;Brian J. Harvey;K. Hayes;Matthew R. Helmus;R. Hensley;K. Hondula;T. Huang;Wiley J. Hundertmark;Virginia Iglesias;P. Jacinthe;L. Jansen;Marta A. Jarzyna;T. M. Johnson;Katherine D. Jones;Megan A. Jones;M. G. Just;Y. Kaddoura;Aurora K. Kagawa‐Vivani;A. Kaushik;A. B. Keller;Katelyn B. S. King;J. Kitzes;M. J. Koontz;P. Kouba;Wai-Yin Kwan;J. M. LaMontagne;E. LaRue;Daijiang Li;Bonan Li;Yang Lin;D. Liptzin;William Long;Adam L. Mahood;S. Malloy;S. Malone;J. McGlinchy;C. Meier;B. Melbourne;Nathan P. Mietkiewicz;J. Morisette;Moussa Moustapha;C. Muscarella;J. Musinsky;R. Muthukrishnan;K. Naithani;M. Neely;Kari E. A. Norman;S. Parker;Mariana Perez Rocha;L. Petri;Colette A. Ramey;S. Record;M. Rossi;M. SanClements;Victoria M. Scholl;A. Schweiger;B. Seyednasrollah;D. Sihi;Kathleen R. Smith;Eric R Sokol;S. Spaulding;Anna I. Spiers;Lise A. St. Denis;Anika P. Staccone;Kaitlin Stack Whitney;D. Stanitski;E. Stricker;Thilina D. Surasinghe;Sarah K. Thomsen;P. M. Vasek;Liao Xiaolu;Di Yang;Rong Yu;Kelsey M. Yule;K. Zhu

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

• DOI: 10.1002/ecs2.3795
• 发表时间: 2021-11-01
• 期刊: ECOSPHERE
• 影响因子: 2.7
• 作者: Kitzes, Justin;Blake, Rachael;Yule, Kelsey
• 通讯作者: Yule, Kelsey