Geology topics

Nancy F. Glenn

Publications and source records attributed to Nancy F. Glenn.

13 recordsLinked to original sources

USGS2023

2019 Forest Service–NASA Joint Applications Workshop: Satellite data to support natural resource management: A framework for aligning NASA products with land management agency needs

In 2019, about 103 participants from the U.S. Department of Agriculture’s Forest Service (Forest Service), the National Aeronautics and Space Administration (NASA) and other federal, private, and academic entities attended the Forest Service–NASA Joint Applications Workshop. The objective of this workshop was to increase awareness and understanding of the capabilities of NASA data products, and strengthen partnerships between NASA and the Forest Service. In this vein, the workshop sought to identify opportunities for collaboration between the Forest Service and NASA on the topics of (1) soil moisture and hydrology, (2) carbon emissions and flux, and (3) vegetation structure and function. Four key high-level opportunities for increased coordination and collaboration were identified including: (1) develop a strategic framework for collaboration and coordination; (2) establish working groups and engage in early adopter programs; (3) distill needs requirements and conduct feasibility studies; and (4) integrate tools and data. Although the workshop was conducted 4 years ago, the information is still highly relevant, since GTAC (Appendix B) and the SMAP, NISAR, ICESAT-2, CMS Programs are all still very active and numerous points of collaboration between the Forest Service and NASA have occurred.

General Technical Report↗

USGS2021

Performance of the ecosystem demography model (EDv2.2) in simulating gross primary production capacity and activity in a dryland study area

Dryland ecosystems play an important role in the global carbon cycle, including regulating the inter-annual global carbon sink. Dynamic global vegetation models (DGVMs) are essential tools that can help us better understand carbon cycling in different ecosystems. Currently, there is limited knowledge of the performance of these models in drylands partly due to characterizing the heterogeneity of the vegetation and hydrometeorological conditions. The aim of this study is to evaluate the performance of a DGVM for drylands to facilitate improved understanding of gross primary production (GPP) as one of the important components of the carbon cycle. We performed a sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) DGVM to simulate GPP in a dryland watershed (Reynolds Creek Experimental Watershed, Idaho) in the western US for the years 2000-2017. GPP capacity and activity were investigated by comparing model simulations with GPP estimated from eddy covariance data (available from 2015-2017) and remote sensing products (2000-2017). Our results show good performance of EDv2.2 at daily timesteps ( R M S E ≈ 0.38 [ kgC / m 2 / year ] ) "> RMSE≈0.38[kgC/m2/year])

Idaho↗

USGS2021

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. This effort synthesizes the findings of more than 130 scientists.

Remote Sensing of Environment↗

USGS2020

Understanding the effect of fire on vegetation composition and gross primary production in a semi-arid shrubland ecosystem using the Ecosystem Demography (EDv2.2) model

Wildfires in sagebrush (Artemisia spp.) dominated semi-arid ecosystems in the western United States have increased dramatically in frequency and severity in the last few decades. Severe wildfires often lead to the loss of native sagebrush communities and change the biogeochemical conditions which make it difficult for sagebrush to regenerate. Invasion of cheat- grass (Bromus tectorum) accentuates the problem by making the ecosystem more susceptible to frequent burns. Managers have implemented several techniques to cope with the cheatgrass-fire cycle, ranging from controlling undesirable fire effects by removing fuel loads either mechanically or via prescribed burns, to seeding the fire-affected areas with shrubs and native perennial forbs. There have been a number of studies at local scales to understand the direct impacts of wildfire on vegetation, however, there is a larger gap in understanding these impacts at broad spatial and temporal scales. This need highlights the importance of dynamic global vegetation models (DGVMs) and remote sensing. In this study, we explored the influence of fir on vegetation composition and gross primary production (GPP) in the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, a dynamic global vegetation model. We selected Reynolds Creek Experimental Watershed (RCEW) to run our simulation study, an intensively monitored sagebrush-dominated ecosystem in the northern Great Basin. We ran point-based simulations at four existing flux-tower sites in the study area for a total 150 years after turning on the fire module in the 25th year. Results suggest dominance of shrubs in a non-fire scenario, however under the fire scenario we observed contrasting phases of high and low shrub density and C3 grass growth. Regional model simulations showed a gradual decline in GPP for fire-introduced areas through the initial couple of years instead of killing all the vegetation in the affected area in the first year itself. We also compared the results from EDv2.2 with satellite-derived GPP estimates for the areas in RCEW burned by a wildfire in 2015 (Soda Fire). We observed moderate pixel-level correlations between maps of post-fire recovery EDv2.2 GPP and MODIS derived GPP. This study contributes to understanding the application of ecosystem models to investigate temporal dynamics of vegetation under alternative fire regimes and post-fire ecosystem restoration.

Idaho↗

USGS2019

Modeling long-term effects of fuel treatments on fuel loads and fire regimes in the Great Basin

The principal motivation for this study is that sagebrush-steppe ecosystems are undergoing significant state changes, and land managers are challenged with optimizing their resources for both short- and long-term use. Yet, limited knowledge is available regarding how the sagebrush-steppe will respond to environmental changes related to precipitation and temperature regimes, and disturbance such as fire. Furthermore, there is a lack of understanding on how fuels reduction and other fuel management activities will impact these ecosystems over the long-term. We addressed these challenges by adapting and testing a vegetation dynamics model, the Ecosystem Demography v2.2 model (EDv2.2), for the sagebrush-steppe. Vegetation dynamics models can provide estimations of ecosystem productivity in their natural and disturbance states, and thus serve as a tool to understand and predict potential changes in various processes and properties of vegetation communities. Yet, there is no vegetation dynamics model that is well-developed for the sagebrush-steppe, and thus significant effort is needed to test EDv2.2 for its application. As part of our efforts to develop the EDv2.2 model into a useful tool for the sagebrush-steppe, we developed a sagebrush plant functional type (PFT) as part of this study, and then performed sensitivity analyses, model calibration, and finally model evaluation. Furthermore, we developed several model scenarios under natural (undisturbed) and disturbed (fire) environments. We compared our model outputs with ground-based data (field and eddy covariance) and remote sensing observations. The results of our project include a sagebrush PFT that can be used in both future EDv2.2 modeling efforts and other vegetation dynamic models. Our results from the model sensitivity analysis indicate that specific leaf area (SLA), stomatal slope (STO_S), cuticular conductance (CUT_C), and carboxylase rate constant (VM0) are sensitive parameters to vegetation productivity in the model (based on gross primary production, GPP), and future modeling efforts will benefit from both lab and field studies of these parameters and sensitivity analyses. Through calibration, we found that the EDv2.2 model estimates of GPP were modeled well at our lowest elevation field site in Reynolds Creek Experimental Watershed (RCEW), which is dominated by Wyoming big sagebrush. On the contrary, we found poorer results at higher elevation site shrub sites. These sites are characterized by either low sagebrush or mountain big sagebrush, and have more forb cover than the low elevation site. In this project we also implemented the fire model in EDv2.2 to explore how shrub and C3 grasses respond to fire by analyzing post-fire GPP. We ran both point and regional model runs with fire introduced. In most fire scenarios, fire substantially reduced shrub GPP and it took several decades for shrub GPP to return to pre-fire conditions. Grass GPP responded more quickly in post-fire conditions. While these processes are representative of what other studies have found, significant efforts to improve the fire processes in EDv2.2 are needed. For example, nuances associated with the fire subroutine in the model (running periodic fire events versus instantaneous fires and fire intensity) will need to be expanded. Another significant contribution to our knowledge gap is that additional PFTs to represent the sagebrush-steppe (e.g. annual grasses such cheatgrass) are needed for EDv2.2. Regardless, this project made significant advances in PFT development and model testing. Moreover, the EDv2.2 provides a useful framework to conceptualize vegetation dynamics, project future conditions, and consider fire as a disturbance. With additional parameterizations, PFTs, and fire routines, EDv2.2 will evolve as a tool for which to better understand future ecosystem dynamics of the sagebrush-steppe.

Idaho↗

USGS2018

Fuels guide and database for intact and invaded big sagebrush (Artemisia tridentata) ecological sites—User manual

The Fuels Guide and Database (FGD) is intended to provide fuel loading and vegetation information for big sagebrush ( Artemisia tridentata ) ecological sites in the Morley Nelson Snake River Birds of Prey National Conservation Area (hereinafter the NCA) in southern Idaho. Sagebrush ecosystems in the NCA and throughout much of the Great Basin are highly influenced by non-native plants that alter successional trajectories and promote frequent wildfires, especially due to fine-fuel loadings that are highly variable over time and space. These dynamic fuel conditions can increase uncertainty when attempting to project fire risk and fire behavior. The FGD was developed to help quantify and assess these dynamic fuel loadings, and it provides access to fuels data across a range of conditions, from relatively intact sagebrush-bunchgrass communities to degraded communities dominated by nonnative annual grasses and forbs. The FGD can be queried for a variety of environmental conditions, and it provides tabular data, reports, and photographic records of fuels based on user queries. This report describes the FGD, including overall data content and data-collection methods, as well as instructions for installing and using the database.

Data Series↗

USGS2018

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R 2 = 0.77, RMSE = 7%), annual grasses (R 2 = 0.70, RMSE = 21%), perennial grasses (R 2 = 0.36, RMSE = 12%), forbs (R 2 = 0.52, RMSE = 6%), bare earth or litter (R 2 = 0.49, RMSE = 19%), and the biomass of shrubs (R 2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R 2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

Idaho↗

USGS2017

Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland

The aerodynamic roughness length (Z 0 m ) serves an important role in the flux exchange between the land surface and atmosphere. In this study, airborne lidar ( ALS ), terrestrial lidar ( TLS ), and imaging spectroscopy data were integrated to develop and test two approaches to estimate Z 0 m over a shrub dominated dryland study area in south-central Idaho, USA. Sensitivity of the two parameterization methods to estimate Z 0 m was analyzed. The comparison of eddy covariance-derived Z 0 m and remote sensing-derived Z 0 m showed that the accuracy of the estimated Z 0 m heavily depends on the estimation model and the representation of shrub (e.g., Artemisia tridentata subsp. wyomingensis) height in the models. The geometrical method (RA1994) led to 9 percent (~0.5 cm) and 25% (~1.1 cm) errors at site 1 and site 2, respectively, which performed better than the height variability-based method (MR1994) with bias error of 20 percent and 48 percent at site 1 and site 2, respectively. The RA1994 model resulted in a larger range of Z 0 m than the MR1994 method. We also found that the mean, median and 75th percentiles of heights (H75) from ALS provides the best Z 0 m estimates in the MR1994 model, while the mean, median, and MLD (Median Absolute Deviation from Median Height), as well as AAD (Mean Absolute Deviation from Mean Height) heights from ALS provides the best Z 0 m estimates in the RA1994 model. In addition, the fractional cover of shrub and grass, distinguished with ALS and imaging spectroscopy data, provided the opportunity to estimate the frontal area index at the pixel-level to assess the influence of grass and shrub on Z 0 m estimates in the RA1994 method. Results indicate that grass had little effect on Z 0 m in the RA1994 method. The Z 0 m estimations were tightly coupled with vegetation height and its local variance for the shrubs. Overall, the results demonstrate that the use of height and fractional cover from remote sensing data are promising for estimating Z 0 m , and thus refining land surface models at regional scales in semiarid shrublands.

Photogrammetric Engineering and Remote Sensing↗

USGS2016

Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R 2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Remote Sensing of Environment↗

USGS2016

The integrated rangeland fire management strategy actionable science plan

The Integrated Rangeland Fire Management Strategy (hereafter Strategy, DOI 2015) outlined the need for coordinated, science-based adaptive management to achieve long-term protection, conservation, and restoration of the sagebrush (Artemisia spp.) ecosystem. A key component of this management approach is the identification of knowledge gaps that limit implementation of effective strategies to meet current management challenges. The tasks and actions identified in the Strategy address several broad topics related to management of the sagebrush ecosystem. This science plan is organized around these topics and specifically focuses on fire, invasive plant species and their effects on altering fire regimes, restoration, sagebrush and greater sage-grouse (Centrocercus urophasianus), and climate and weather.

Report↗

USGS2015

Quantifying and predicting fuels and the effects of reduction treatments along successional and invasion gradients in sagebrush habitats

Sagebrush shrubland ecosystems in the Great Basin are prime examples of how altered successional trajectories can create dynamic fuel conditions and, thus, increase uncertainty about fire risk and behavior. Although fire is a natural disturbance in sagebrush, post-fire environments are highly susceptible to conversion to an invasive grass-fire regime (often referred to as a “grass-fire cycle”). After fire, native shrub-steppe plants are often slow to regenerate, whereas nonnative annuals, especially cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), can establish quickly and outcompete native species. Once fire-prone annuals become established, fire occurrences increase, further promoting dominance of nonnative species. The invasive grass-fire regime also alters nutrient and hydrologic cycles, pushing ecosystems beyond ecological thresholds toward steady-state, fire-prone, nonnative communities. These changes affect millions of hectares in the Great Basin and increase fire risk, decrease habitat quality and biodiversity, accelerate soil erosion, and degrade rangeland resources for livestock production. In many sagebrush landscapes, constantly changing plant communities and fuel conditions hinder attempts by land managers to predict and control fire behavior, restore native communities, and provide ecosystem services (e.g., forage production for livestock). We investigated successional and nonnative plant invasion states and associated fuel loads in degraded sagebrush habitat in a focal study area, the Morley Nelson Snake River Birds of Prey National Conservation Area (hereafter the NCA), in the Snake River Plain Ecoregion of southern Idaho. We expanded our inference by comparing our findings to similar data collected throughout seven major land resource areas (MLRAs) across the Great Basin (JFSP Project “Fire Rehabilitation Effectiveness: A Chronosequence Approach for the Great Basin” [09-S-02-1]). 4 We used a combination of field-sampling, experimental treatments, and remotely sensed data to address the following questions: (1) How do fuel loads change along gradients of succession and invasion in sagebrush ecological sites? (2) How do fuel reduction treatments influence fuels in invaded areas formerly dominated by sagebrush? (3) How do fuel loads vary across landscapes and which remote sensing techniques are effective for characterizing them?

Idaho↗

USGS2012

Transport of biologically important nutrients by wind in an eroding cold desert

Wind erosion following fire is an important landscape process that can result in the redistribution of ecologically important soil resources. In this study we evaluated the potential for a fire patch in a desert shrubland to serve as a source of biologically important nutrients to the adjacent, downwind, unburned ecosystem. We analyzed nutrient concentrations (P, K, Ca, Mg, Cu, Fe, Mn, Al) in wind-transported sediments, and soils from burned and adjacent unburned surfaces, collected during the first to second growing seasons after a wildfire that burned in 2007 in Idaho, USA in sagebrush steppe; a type of cold desert shrubland. We also evaluated the timing of potential wind erosion events and weather conditions that might have contributed to nutrient availability in downwind shrubland. Findings indicated that post-fire wind erosion resulted in an important, but transient, addition of nutrients on the downwind shrubland. Aeolian sediments from the burned area were enriched relative to both the up- and down-wind soil and indicated the potential for a fertilization effect through the deposition of the nutrient-enriched sediment during the first, but not second, summer after wildfire. Weather conditions that could have produced nutrient transport events might have provided increased soil moisture necessary to make nutrients accessible for plants in the desert environment. Wind transport of nutrients following fire is likely important in the sagebrush steppe as it could contribute to pulses of resource availability that might, for example, affect plant species differently depending on their phenology, and nutrient- and water-use requirements.

Idaho↗

USGS2012

Remote sensing of sagebrush canopy nitrogen

This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R 2 value of 0.72 and an R 2 predicted value of 0.42 ( n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R 2 to 0.95 ( R 2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

Remote Sensing of Environment↗