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中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。目前正在发生的大规模灭绝事件比化石记录中记录的先前事件要大得多。人类被广泛认为是在一个新的时期创造的人类世时代的灭绝率和我们的社会在地球上记录的足迹？s表面。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，并在世界范围内日益成为优先事项，这并不奇怪。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在地面上使用计算方法来测量，例如一个地区的物种总数，或物种数量按每个物种覆盖的面积加权。全球生物多样性趋势可以从卫星上收集，但如果不扩大对同一物种进行短期（每1至5年）地面和空中观测的网络，就无法研究更精细的过程。国家生态观测网的遥感和地面测量非常适合填补这一知识空白。该奖项将结合联合收割机从地面和网络的空中观测平台同时收集的测量数据，该平台具有先进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测绘。一种测量生物多样性的方法将在位于沿着美国东部温带、阔叶和混交林纬度梯度的八个网络站点进行演示。项目活动将在一所私立本科院校进行两年。将举办两个研讨会，旨在通过与生态研究作为教育网络参与者的合作，让教师和学生参与到一个初级本科院校网络中。将编制的课程侧重于对学生进行实地数据分析培训、大型数据集处理、宏观生态学理论和遥感方面的跨学科培训。增值遥感地图产品，教育材料和生物多样性处理框架，从这个项目产生的将提供给国家生态观测站网络数据用户测试其他网站位于其他生态气候领域在美国大陆。记录和理解模式的生物多样性是在宏观生态学的核心问题。到目前为止，从大陆到全球范围的植物多样性绘图一直是以发生数据为基础的，由于取样不均匀而有偏见，而且只狭隘地侧重于物种丰富度。植被多样性或植物多样性沿着一系列时间、生物和空间尺度发生，从基因到物种、生态系统和全球生物多样性。局部景观中的生物多样性斑块或走廊可以反映出物种多样性的细尺度模式与环境变量所代表的大尺度动态之间的机械关系。该项目的第一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（光谱变异假设）相关。该项目的第二个组成部分解决了跨尺度的相互作用，并将通过增强与空间异质性相关的缩放挑战以及细尺度物种多样性和更广泛的环境变量之间的相互作用理论直接受益于宏观系统生物学。该项目调查以下三个问题：1）利用网络航空观测平台数据集进行物种多样性制图的空间和光谱检测极限是什么，以及简单的物种多样性升级有多有限，2）什么样的粗尺度自然和人类变量正在驱动站点级别的物种多样性模式，以及预测在哪里出现局部多样性热点，3）物种多样性的格局和控制如何沿沿着大尺度梯度变化，空间异质性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和分类多样性之间关系的新的和天气探索提供基础。光学多样性产品还可用于运行模拟，以评估未来卫星光谱任务在绘制生物多样性基本变量方面的作用。通过该项目开发的总体处理流程将为确定潜在景观和遗传多样性热点创造新的方法，其结果可用于测试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