Geology topics

Alexey N. Shiklomanov

Publications and source records attributed to Alexey N. Shiklomanov.

6 recordsLinked to original sources

USGS2026

Restoration based on cost-benefit optimization: A grasslands pilot study

Ecological restoration is essential to meeting global biodiversity conservation goals. Given limited conservation budgets, deciding where to restore habitat is a key challenge for the coming decade. We developed a spatially explicit framework to optimize ecological restoration site selection by integrating land use history, species distributions, and economic costs. The framework includes the following steps: identify potential restoration area based on relevant environmental measures like land use; identify species of interest; calculate restoration benefits by modeling habitat and climate suitability and estimating reduced extinction risk associated with restoring a particular land parcel based on a modified species–area relationship; aggregate benefits across species; and compare to parcel-level land acquisition costs. We applied linear programming to maximize conservation benefit/restoration cost ratios to identify optimized restoration sites. We illustrate this approach using a case study for highly threatened grassland ecosystems in the Great Plains region of Kansas, USA. We selected five grassland animal species (greater prairie chickens [ Tympanuchus cupido ], lesser prairie chickens [ Tympanuchus pallidicinctus ], swift fox [ Vulpes velox ], pronghorn [ Antilocapra americana ], and regal fritillary [ Speyeria idalia ]) as indicators of restoration benefit across taxa. For the indicator species that we chose, shortgrass and mixed-grass prairies had the highest conservation benefit to cost ratio. Setting a minimum restoration threshold for each habitat type allowed us to identify high-priority tallgrass prairie sites. Despite increasing interest in ecological restoration, optimizing restoration site selection is challenging because one must consider habitat features that do not currently exist. The modeling approach described here is flexible and can be updated for different ecosystems, species, and conservation priorities. We outline potential alterations that can be made in future analyses, depending on desired restoration goals.

Kansas↗

USGS2025

Near real-time indicators of burn severity in the western U.S. from active fire tracking

Background Timely information on wildfire burn severity is critical to assess and mitigate potential post-fire impacts on soils, vegetation, and hillslope stability. Tracking individual fire spread and intensity using satellite active fire data provides a pathway to near real-time (NRT) information. Here, we generated a large database ( n = 2177) of wildfire events in the western United States (U.S.) between 2012 and 2021 using active fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Fire Events Data Suite (FEDS) algorithm to track large fire growth every 12 h. We integrated fire tracking data with final fire perimeters and burn severity data from the Monitoring Trends in Burn Severity (MTBS) program to evaluate the relationship between burn severity and fire behavior metrics derived from the fire tracking approach, including the rate of fire spread and average fire radiative power (FRP) of fire detections for each 12-h growth increment. Results When stratified by vegetation type, FRP and rate of spread metrics were positively correlated with classified burn severity for each 12-h growth increment, highlighting the potential to rapidly identify areas of high and low severity burning. In forests, integrated measures of FRP over the fire lifetime captured persistent flaming and smoldering that compensated for initial differences between AM (01:30) and PM (13:30) fire detections. Predictive modeling of these relationships based on multiple fire behavior indicators and vegetation type from the LANDFIRE program yielded an accuracy of 78% for the separation of unburned/low and moderate/high burn severity classes. Conclusions These results demonstrate the ability to capture within-fire differences in burn severity using NRT indicators from fire tracking to assist with emergency management and disaster preparedness for post-fire hazards, such as landslides, debris flows, or changes in stream flow and water quality. As VIIRS data are available within minutes of each satellite overpass in the U.S., rapid estimates of burn severity based on fire tracking can be made days or weeks before a large wildfire is fully contained.

western United States↗

USGS2025

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗

USGS2022

Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States

Aim Climate change is an increasingly important driver of biodiversity loss. The ectothermic nature of amphibians may make them particularly sensitive to changes in temperature and precipitation regimes, adding to declines from other threats. While active season environmental conditions can influence growth and survival, effects of variation in winter conditions on population dynamics are less well-studied. Given that extreme winter temperatures can influence amphibian survival and fitness, we expected that increased winter severity—as measured by variability in winter temperatures and snow cover—would be associated with decreased occupancy, and that populations that experience more severe winters would have the largest sensitivities and show the greatest declines. Location Eastern United States. Time period 2001–2015. Major taxa studied Anurans. Methods We used large-scale citizen science data from the eastern half of the United States, a diverse biogeographic and climatic region, to assess how variation in winter severity influenced occupancy dynamics (i.e. presence or absence of species across sites and years) of 11 spring and summer breeding anuran species. Results Most species had increased occupancy in years with greater than average snow cover and warmer than average mean winter temperatures. Surprisingly, climatic conditions in winter affected occupancy dynamics of species with varying life history characteristics, including both spring and summer breeding species, those that overwinter under the soil, and those that overwinter in ponds and stream beds. For two wide-ranging species ( Lithobates catesbeianus and Lithobates clamitans ), colder winter temperatures reduced occupancy more at northern latitudes, while the association between days of snow cover and latitude was equivocal. Main conclusions As the climate continues to change, expected reductions in snowpack may reduce occupancy of already declining anuran populations, while milder winters may improve overwinter survival for some species. The contradictory impacts of temperature and snow cover illustrate the importance of considering multi-dimensional impacts of climate change on anuran populations.

Diversity and Distributions↗

USGS2021

A new approach to evaluate and reduce uncertainty of model-based biodiversity projections for conservation policy formulation

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.

BioScience↗

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↗