Detecting Fire and Grazing Patterns in Tallgrass Prairie Using Spectral Mixture Analysis

Global grasslands are typically under management practices (such as fire and grazing) that alter nutrient cycling, ecosystem composition, and distribution of organic matter from the unmanaged condition. We evaluated landscape-level response to fire and grazing treatments in the Konza Tallgrass Prairie Research Natural Area, Kansas, using spectral mixture analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired 31 August 1990. Spectral mixture analysis derives the fractional abundances of spectrally unique components in the landscape. The reflectance spectra of these components are called endmembers. Endmember fractions values were compared against ground values of live biomass, current standing dead biomass, and litter for 12 watersheds. Analysis of variance (ANOVA) was performed on 37 watersheds with known burning and grazing histories for each of the remote sensing variables. Seven endmembers were selected from the AVIRIS data using a manual endmember selection method: nonphotosynthetic vegetation (NPV), soil, rock, shade, and three green vegetation endmembers (GV1, GV2, and GV3). Each vegetation endmember correlated differently to biomass measurements and revealed unique relationships to management treatments. From regressions, ANOVAs, and image analysis, these three endmembers were inferred to represent canopy vertical structure or leaf area index (LAI), greenness, and fractional cover of grass, respectively. There was a stronger relationship between the sum of GV1 and GV3 fractions and live grass biomass values than there was with the (unsummed) individual fractions. In an ANOVA, the sum separated both burn and grazing treatments as well as the treatment interaction. The NPV fraction was strongly correlated with ground measurements of litter and standing dead biomass, and significantly separated burn treatments. The soil fraction differentiated grazing treatments, and analysis of the soil fraction image revealed a spatial coherence of grazing patterns along drainages. Similar analyses were perfomed on the Normalized Difference Vegetation Index (NDVI), a commonly used two-band index computed from red and near-infrared reflectance. NDVI, shown in previous studies to estimate the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), was a poor indicator of canopy biomass, but it successfully separated fire treatments. Broad-scale assessment of the state and structure of managed grassland systems requires the identification of several indicator variables. Spectral mixture analysis, unlike NDVI, not only separated treatments but also allowed for the identification of five remotely sensible factors affected by the management treatments, namely, vertical structure, percentage cover or patchiness, greenness, and distribution of soil and litter

A comparison of spectral mixture analysis an NDVI for ascertaining ecological variables

In this study, we compare the performance of spectral mixture analysis to the Normalized Difference Vegetation Index (NDVI) in detecting change in a grassland across topographically-induced nutrient gradients and different management schemes. The Konza Prairie Research Natural Area, Kansas, is a relatively homogeneous tallgrass prairie in which change in vegetation productivity occurs with respect to topographic positions in each watershed. The area is the site of long-term studies of the influence of fire and grazing on tallgrass production and was the site of the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) from 1987 to 1989. Vegetation indices such as NDVI are commonly used with imagery collected in few (less than 10) spectral bands. However, the use of only two bands (e.g. NDVI) does not adequately account for the complex of signals making up most surface reflectance. Influences from background spectral variation and spatial heterogeneity may confound the direct relationship with biological or biophysical variables. High dimensional multispectral data allows for the application position of techniques such as derivative analysis and spectral curve fitting, thereby increasing the probability of successfully modeling the reflectance from mixed surfaces. The higher number of bands permits unmixing of a greater number of surface components, separating the vegetation signal for further analyses relevant to biological variables

Quantifying Grassland-to-Woodland Transitions and the Implications for Carbon and Nitrogen Dynamics in the Southwest United States

Replacement of grasslands and savannas by shrublands and woodlands has been widely reported in tropical, temperate and high-latitude rangelands worldwide (Archer 1994). These changes in vegetation structure may reflect historical shifts in climate and land use; and are likely to influence biodiversity, productivity, above- and below ground carbon and nitrogen sequestration and biophysical aspects of land surface-atmosphere interactions. The goal of our proposed research is to investigate how changes in the relative abundance of herbaceous and woody vegetation affect carbon and nitrogen dynamics across heterogeneous savannas and shrub/woodlands. By linking actual land-cover composition (derived through spectral mixture analysis of AVIRIS, TM, and AVHRR imagery) with a process-based ecosystem model, we will generate explicit predictions of the C and N storage in plants and soils resulting from changes in vegetation structure. Our specific objectives will be to (1) continue development and test applications of spectral mixture analysis across grassland-to-woodland transitions; (2) quantify temporal changes in plant and soil C and N storage and turnover for remote sensing and process model parameterization and verification; and (3) couple landscape fraction maps to an ecosystem simulation model to observe biogeochemical dynamics under changing landscape structure and climatological forcings

Cyberinfrastructure Deployments on Public Research Clouds Enable Accessible Environmental Data Science Education

Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline

Novel Susceptibility Locus at 22q11 for Diabetic Nephropathy in Type 1 Diabetes

Peer reviewe

Supplement 1. Species list, with cumulative abundances by site.

<h2>File List</h2><blockquote> <p><a href="cumulative_assemblage.txt">cumulative_assemblage.txt</a></p> </blockquote><h2>Description</h2><blockquote> <p>The file cumulative_assemblage.txt is a tab-delimited file. It contains the cumulative abundances of each species collected at each site over all rounds of sampling. Column headings are site codes (see <a href="appendix-A.htm">Appendix A</a> for full site names, areas, dates, etc.)</p> <p>*morphospecies determinations based on: Cockerell, T. D. A. 1907. The bees of Boulder County, Colorado. University of Colorado Studies 4(4):239–259.</p> </blockquote

Resilience of a novel ecosystem after the loss of a keystone species: plague epizootics and urban prairie dog management

In a complex urban-impacted landscape, native black-tailed prairie dogs (Cynomys ludovicianus) amplify the trajectory at which grassland plant communities deviate from historical configurations. Prairie dog removal has been proposed as an intervention method based upon the premise that removing a major directional driver of change will initiate the recovery of historically common plant communities. However, in a heavily anthropogenically influenced landscape with a matrix containing only small fragmented areas of native vegetation, the recolonization speed and success of native plant species may not match those observed in less anthropogenically influenced landscapes. This study examined the effect of urban prairie dog removal by using remote sensing to observe the response of plant communities near Boulder, Colorado, USA to plague epizootics. We used Mann-Whitney U tests to compare the soil adjusted vegetation index (SAVI) values from colonies recently extirpated by plague to both areas unoccupied by prairie dogs and to plague-absent colonies. Analysis of 67 Landsat images in three growing season subsets suggested that prairie dog removal alone does not return colony plant communities to compositions representative of grassland areas unoccupied by prairie dogs. The absence of SAVI value changes in the mid- and late-growing seasons suggested that novel vegetation communities on urban prairie dog colonies were highly resilient systems, and prairie dog removal alone was insufficient for restoration. Furthermore, increased early season SAVI values on extirpated colonies could indicate a proliferation of introduced winter active species and exotic forbs, not the desired reemergence of native species, but rather species expected given current climatic changes. Intensive management efforts appear necessary for overcoming the thresholds required to restore urban prairie dog colonies to their historical compositions, an effort made increasingly more difficult with the ongoing effects of other global change drivers

Ecological Research Needs from Multiangle Remote Sensing Data

Remotely sensed land surface reflectance depends upon changing sun and sensor viewing geometry, and this dependence is governed by the bidirectional reflectance distribution function (BRDF). Because the reflectance distribution of vegetation is strongly anisotropic, multi-view angle (MVA) observations of terrestrial ecosystems contain additional and unique information beyond that acquired with nadir or single-angle spectral measurements alone. With the NASA EOS instruments MODIS and MISR and France\u27s POLDER, new capabilities in MVA remote sensing will become widely available for ecological, biogeochemical, and land-surface biophysical research. However, a communication gap exists between the remote sensing and ecological communities in terms of the capabilities of the former and the needs of the latter. In this article, we present a summary of ecological research needs for remotely sensed data. Based on these needs, we present a review of some of the most promising MVA remote sensing methods for fulfilling these requirements. With this article, we hope to facilitate increased communication between the remote sensing, ecological, and biogeochemical research communities