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分为与生命进化的重大变化相对应的地质时期，例如白垩纪恐龙的大规模灭绝。目前正在发生大规模灭绝事件，其数量级比化石记录中记录的先前事件要大得多。由于灭绝率和我们的社会在地球表面记录的足迹，人们普遍认为人类正处于一个被称为“人类世”的新时期。因此，物种数量和生物多样性模式与人类生存的关系日益密切，并在全世界范围内日益受到重视，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在地面上进行测量，例如一个区域内的物种总数，或按每个物种覆盖的面积部分加权的物种数量。全球生物多样性趋势可以通过卫星收集，但如果没有在短期内（每 1 至 5 年）扩大地面和空中对同一物种的观测网络，就无法研究更精细尺度的过程。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空白。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，该平台具有尖端的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绘图。测量生物多样性的方法将在位于美国东部温带阔叶林和混交林纬度梯度上的八个网络站点进行演示。项目活动将在一个主要本科院校进行两年多的时间。将举办两个研讨会，旨在通过与作为教育网络参与者的生态研究合作，让主要本科院校网络中的教师和学生参与进来。将开发的课程重点关注学生在现场数据分析培训、大数据处理、宏观生态学理论和遥感方面的跨学科培训。该项目生成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网络数据用户，用于测试位于美国大陆其他生态气候域的其他站点。记录和理解生物多样性模式是宏观生态学的一个中心问题。迄今为止，大陆到全球范围的植物多样性绘图一直基于发生数据，由于采样不均匀而产生偏差，并且仅关注物种丰富度。植被多样性或植物多样性发生在一系列时间、生物和空间尺度上，从基因到物种、生态系统和全球生物多样性。当地景观中生物多样性的斑块或走廊可以反映物种多样性的精细尺度模式与环境变量所代表的大尺度动态机械相关的区域。该项目的第一个组成部分探讨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（光谱变化假设）。该项目的第二个组成部分解决了跨尺度相互作用，并将通过增强与空间异质性相关的尺度挑战以及跨宏观尺度梯度（如纬度和冰川期以来的时间）之间的细尺度物种多样性和更广泛的环境变量之间的相互作用的理论，直接使宏观系统生物学受益。该项目研究以下三个问题：1）利用网络空中观测平台数据集绘制物种多样性图的空间和光谱检测限制是什么，以及简单的物种多样性升级的限制有多大；2）哪些较粗尺度的自然和人类变量正在驱动地点层面的物种多样性模式，以及预计会出现的局部多样性热点在哪里；3）物种多样性的模式和控制如何沿着宏观尺度梯度变化 空间异质性如何影响宏观系统绘图和建模？根据该奖项生成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和分类多样性之间关系的新的综合探索提供基础。光学分集产品还可用于运行模拟，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方面的作用。通过该项目开发的总体处理流程将创建识别潜在景观和遗传多样性热点的新方法，其结果可用于测试与纬度和海拔分布相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