Engineering topics

Dahlin, Kyla M.

Publications and source records attributed to Dahlin, Kyla M..

7 records

DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Modeled Surface-Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks

The eddy covariance (EC) method is one of the most widely used approaches to quantify surface-atmosphere fluxes. However, scaling up from a single EC tower to the landscape remains an open challenge. To address this, we used 63 site years of data to examine simulated annual and growing season sums of carbon fluxes from three paired land-cover type sites of corn, restored-prairie, and switchgrass ecosystems. This was also done across the landscape by modeling fluxes using different land-cover type input data. An artificial neural network (ANN) approach was used to model net ecosystem exchange (NEE), ecosystem respiration (R eco ), and gross primary production (GPP) at one paired site using environmental observations from the second site only. With a mean spatial separation of 11 km between paired sites, we were able to model annual sums of NEE, R eco , and GPP with uncertainties of 20%, 22%, and 8%, respectively, relative to observation sums. When considering the growing season only, model uncertainties were 17%, 22%, and 9%, respectively for the three flux terms. We also show that ANN models can estimate sums of R eco and GPP fluxes without needing the constraint of similar land-cover-type, with annual uncertainties of 12% and 10%. These results provide new insights to scaling up observations from one EC site beyond the footprint of the EC tower to multiple land-cover types across the landscape.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The fortedata R package: open-science datasets from a manipulative experiment testing forest resilience

The fortedata R package is an open data notebook from the Forest Resilience Threshold Experiment (FoRTE) – a modeling and manipulative field experiment that tests the effects of disturbance severity and disturbance type on carbon cycling dynamics in a temperate forest. Package data consist of measurements of carbon pools and fluxes and ancillary measurements to help analyze and interpret carbon cycling over time. Currently the package includes data and metadata from the first three FoRTE field seasons, serves as a central, updatable resource for the FoRTE project team, and is intended as a resource for external users over the course of the experiment and in perpetuity. Further, it supports all associated FoRTE publications, analyses, and modeling efforts. This increases efficiency, consistency, compatibility, and productivity while minimizing duplicated effort and error propagation that can arise as a function of a large, distributed and collaborative effort. More broadly, fortedata represents an innovative, collaborative way of approaching science that unites and expedites the delivery of complementary datasets to the broader scientific community, increasing transparency and reproducibility of taxpayer-funded science. The fortedata package is available via GitHub: https://github.com/FoRTExperiment/fortedata (last access: 19 February 2021), and detailed documentation on the access, used, and applications of fortedata are available at https://fortexperiment.github.io/fortedata/ (last access: 19 February 2021). The first public release, version 1.0.1 is also archived at https://doi.org/10.5281/zenodo.4399601 (Atkins et al., 2020b). All data products are also available outside of the package as .csv files: https://doi.org/10.6084/m9.figshare.13499148.v1 (Atkins et al., 2020c).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017-2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a "Designated Targeted Observable" (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380 - 2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3-5 µm; TIR: 8-12 µm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. Finally, this effort synthesizes the findings of more than 130 scientists.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Geospatial coherence of surface-atmosphere fluxes in the upper Great Lakes region

Surface-atmosphere fluxes are known to vary at multiple time scales, but uncertainty is high as to how fluxes change spatially within regions. With an increase in the number of eddy covariance towers, we are now able to examine the geospatial coherence of ecosystem fluxes, using time-series correlation. Eighteen sites from Michigan and Wisconsin were used in this study, ranging from 100 m to 600 km apart. Surface-atmosphere fluxes from a six-month period were used to quantify spatial coherence on a pair-wise basis. Using geospatial statistics, carbon and sensible heat (H) fluxes were found to be 95% correlated directly outside of their flux footprint and 56% correlated up to a distance of ~35 km. Latent (LE) and momentum (τ) fluxes were less correlated, 83% directly outside of their flux footprint and 40% at a distance of ~130 km albeit, at a much larger spatial distance than for the carbon and sensible heat fluxes. All fluxes showed strong spectral resonance at diel and seasonal timescales, with 1-, 2- and 3-month periods being common modes of variability among H, LE, and τ fluxes. Results based on Empirical Orthogonal Function show distinct transitions of net ecosystem exchange from fall to winter before photosynthesis or respiration while H and τ do not exhibit coherent trends. Here, this work demonstrates the potential of quantifying geospatial coherence of surface-atmosphere fluxes in the Midwestern United States, with the ability to predict fluxes beyond the spatial limit of a single flux tower footprint. Ultimately, expanding the flux measurements to larger scales would allow better spatial scaling of terrestrial surface-atmosphere fluxes between tower footprint and modeling or remote sensing scales.

54 ENVIRONMENTAL SCIENCES↗