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31 records · Page 2Linked to original sources
USGS2008

Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape

Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist, most of them ratio the reflection of light in the red and NIR sections of the spectrum to separate the landscape into water, soil, and vegetation. Theoretical analyses and field studies have shown that VIs are near-linearly related to photosynthetically active radiation absorbed by a plant canopy, and therefore to light-dependent physiological processes, such as photosynthesis, occurring in the upper canopy. Practical studies have used time-series VIs to measure primary production and evapotranspiration, but these are limited in accuracy to that of the data used in ground truthing or calibrating the models used. VIs are also used to estimate a wide variety of other canopy attributes that are used in Soil-Vegetation-Atmosphere Transfer (SVAT), Surface Energy Balance (SEB), and Global Climate Models (GCM). These attributes include fractional vegetation cover, leaf area index, roughness lengths for turbulent transfer, emissivity and albedo. However, VIs often exhibit only moderate, non-linear relationships to these canopy attributes, compromising the accuracy of the models. We use case studies to illustrate the use and misuse of VIs, and argue for using VIs most simply as a measurement of canopy light absorption rather than as a surrogate for detailed features of canopy architecture. Used this way, VIs are compatible with “Big Leaf” SVAT and GCMs that assume that canopy carbon and moisture fluxes have the same relative response to the environment as any single leaf, simplifying the task of modeling complex landscapes.

Arizona↗
USGS2017

Description of chronostratigraphic units preserved as channel deposits and geomorphic processes following a basin-scale disturbance by a wildfire in Colorado

The consequence of a 1996 wildfire disturbance and a subsequent high-intensity summer convective rain storm (about 110 millimeters per hour) was the deposition of a sediment superslug in the Spring Creek basin (26.8 square kilometers) of the Front Range Mountains in Colorado. Spring Creek is a tributary to the South Platte River upstream from Strontia Springs Reservoir, which supplies domestic water for the cities of Denver and Aurora. Changes in a superslug were monitored over the course of 18 years (1996–2014) by repeat surveys at 18 channel cross sections spaced at nearly equal intervals along a 1,500-meter study reach and by a time series of photographs of each cross section. Surveys were not repeated at regular time intervals but after major changes caused by different geomorphic processes. The focus of this long-term study was to understand the evolution and internal alluvial architecture of chronostratigraphic units (defined as the volume of sediment deposited between two successive surveys), and the preservation or storage of these units in the superslug. The data are presented as a series of 18 narratives (one for each cross section) that summarize the changes, illustrate these changes with photographs, and provide a preservation plot showing the amount of each chronostratigraphic unit still remaining in June 2014. The most significant hydrologic change after the wildfire was an exponential decrease in peak discharge of flash floods caused by summer convective rain storms. In response to these hydrologic changes, all 18 locations went through an aggradation phase, an incision phase, and finally a stabilization phase. However, the architecture of the chronostratigraphic units differs from cross section to cross section, and units are characterized by either a laminar, fragmented, or hybrid alluvial architecture. In response to the decrease in peak-flood discharge and the increase in hillslope and riparian vegetation, Spring Creek abandoned many of the nearly horizontal erosional and depositional surfaces and left a landscape consisting of a series of cut-and-fill terraces as a legacy of this wildfire disturbance.

Colorado↗
USGS2020

An overview of agent-based models in plant biology and ecology

Agent-based modeling (ABM) has become an established methodology in many areas of biology, ranging from the cellular to the ecological population and community levels. In plant science, two different scales have predominated in their use of ABM. One is the scale of populations and communities, through the modeling of collections of agents representing individual plants, interacting with each other and with the environment. The other is the scale of the individual plant, through the modeling, by functional-structural plant models (FSPMs), of agents representing plant building blocks, or metamers, to describe the development of plant architecture and functions within individual plants. The purpose of this review is to show key results and parallels in ABM for growth, mortality, carbon allocation, competition, and reproduction across the scales from the plant organ to populations and communities on a range of spatial scale to the whole landscape. Several areas of application of ABMs are reviewed, showing that some issues are addressed by both population-level ABMs and FSPMs. Continued increase in the relevance of ABM to environmental science and management will be helped by greater integration of ABMs across these two scales.

Annals of Botany↗
USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗
USGS2024

Living with wildfire in Santa Fe: 2021 Data Report

The City of Santa Fe is well known for arts, food, and architecture, but it also faces significant risk of wildfire. In 2020, the City of Santa Fe partnered with the Wildfire Research (WiRē) team with the goal of better understanding the needs of residents within the study area and their level of support for wildfire risk mitigation programs. The resulting project centers on two types of property-level data: rapid wildfire risk assessment data and household survey data. We followed the WiRē Rapid Wildfire Risk Assessment (WiRē RA) protocol, which measures parcel-level risk as the sum of a set of 13 attributes related to access to the property, background fuels and topography, vegetation near the home, and building materials. This report summarizes the results of the study. Overall, the study indicated a community that was engaged in preparing for wildfire yet had more that could be done to reduce its risk. Common architectural styles led to generally hardened structures, and respondents reported many risk reduction activities, yet most properties were found to have significant vulnerabilities related to limited defensible space and combustible materials near and attached to dwellings. Although many survey respondents did not perceive these same vulnerabilities on their own properties, survey results nonetheless demonstrated widespread interest for programs intended to reduce wildfire risk at the landscape, community, and individual parcel scales.

New Mexico↗
USGS2007

Antarctic Multibeam Bathymetry and Geophysical Data Synthesis: an on-line digital data Resource for marine geoscience research in the Southern Ocean

The Antarctic Multibeam Bathymetry and Geophysical Data Synthesis(AMBS) is a web-accessible data resource for marine geoscience research in the Southern Ocean. The primary focusisto preserve and provide public accessto multibeam bathymetry acquired during expeditions of research vesselssupported by the U.S. National Science Foundation. Since itsinception in 2003, our primary goal has been to facilitate visualization and exploration of the subsea landscape to the full detail of the original data by both specialists and non-specialists. Visualization across a widerange of map scales at high latitudesis made possible by dynamic accessto a gridded synthesisin both Polar and Mercator projections. A second goal isto support multi-disciplinary research needs by offering data discovery and visualization of numerous complementary geoscience datasets.In thisreport, we describe the design objectives and architecture of the AMBS, as well asrecent developmentsregarding data submission and delivery via Web Services

Open-File Report↗
USGS2009

Hydroecological factors governing surface water flow on a low-gradient floodplain

"Interrelationships between hydrology and aquatic ecosystems are better understood in streams and rivers compared to their surrounding floodplains. Our goal was to characterize the hydrology of the Everglades ridge and slough floodplain ecosystem, which is valued for the comparatively high biodiversity and connectivity of its parallel-drainage features but which has been degraded over the past century in response to flow reductions associated with flood control. We measured flow velocity, water depth, and wind velocity continuously for 3 years in an area of the Everglades with well-preserved parallel-drainage features (i.e., 200-m wide sloughs interspersed with slightly higher elevation and more densely vegetated ridges). Mean daily flow velocity averaged 0.32 cm s1 and ranged between 0.02 and 0.79 cm s1. Highest sustained velocities were associated with flow pulses caused by water releases from upstream hydraulic control structures that increased flow velocity by a factor of 2–3 on the floodplain for weeks at a time. The highest instantaneous measurements of flow velocity were associated with the passage of Hurricane Wilma in 2005 when the inverse barometric pressure effect increased flow velocity up to 5 cm s1 for several hours. Time-averaged flow velocities were 29% greater in sloughs compared to ridges because of marginally higher vegetative drag in ridges compared to sloughs, which contributed modestly (relative to greater water depth and flow duration in sloughs compared to ridges) to the predominant fraction (86%) of total discharge through the landscape occurring in sloughs. Univariate scaling relationships developed from theory of flow through vegetation, and our field data indicated that flow velocity increases with the square of water surface slope and the fourth power of stem diameter, decreases in direct proportion with increasing frontal area of vegetation, and is unrelated to water depth except for the influence that water depth has in controlling the submergence height of vegetation that varies vertically in its architectural characteristics. In the Everglades the result of interactions among controlling variables was that flow velocity was dominantly controlled by water surface slope variations responding to flow pulses more than spatial variation in vegetation characteristics or fluctuating water depth. Our findings indicate that floodplain managers could, in addition to managing water depth, manipulate the frequency and duration of inflow pulses to manage water surface slope, which would add further control over flow velocities, water residence times, sediment settling, biogeochemical transformations, and other processes that are important to floodplain function."

Water Resources Research↗
USGS2018

Native peoples’ relationship to the California chaparral

Ethnographic interviews and historical literature reviews provide evidence that for many tribes of California, chaparral plant communities were a rich source of food, medicines, and technologies and that they supplemented natural fires with deliberate burning of chaparral to maximize its ability to produce useful products. Many of the most important chaparral plant species used in the food and material culture have strong adaptations to fire. Particularly useful were many annual and perennial herbs, which proliferate after fire from seed and bulb banks, shrub resprouts that made superb cordage and basketry material, as well as animals that were more readily caught in postfire environments. The reasons for burning in chaparral are grouped into seven ecological categories, each relying on a known response to fire of the chaparral community. The authors posit that tribes employed intentional burning to maintain chaparral in different ages and size classes to meet diverse food and material needs, tracking the change in plant and animal abundance and diversity, and shifts in shrub architecture and habitat structure during the recovery of the chaparral community. Areas were burned in ways designed to create a mosaic of open grassland and recently burned, young and mature stands of chaparral with different combinations of species and densities. This management conferred on chaparral plant communities a degree of spatial, structural, successional, and biotic diversity that exceeded what would have been the case in the absence of human intervention. These impacts are still evident on contemporary landscapes.

California↗
USGS2019

Rapid broad-scale ecosystem changes and their consequences for biodiversity

Biodiversity contributes to and depends on ecosystem structure and associated function. Ecosystem structure, such as the amount and type of tree cover, influences fundamental abiotic variables such as near-ground incoming solar radiation (e.g., Royer et al. 2011), which in turn affects species and associated biodiversity (e.g., Trotter et al. 2008). In many systems, foundational, dominant, or keystone species (or species groups) are important in determining biodiversity, often because of their role in determining ecosystem structure. At spatial scales ranging from ecosystems to regions and larger, structural characteristics of vegetation or other structurally dominant organisms such as corals can influence species diversity, whether focused on alpha diversity (mean species diversity at the habitat level), beta diversity (differentiation among habitats), or gamma diversity (total species diversity across a landscape; Whittaker 1960). Climate change is projected to alter ecosystems at broad scales. In many cases, this will be due to extreme climate events such as droughts, floods and hurricanes, the effects of which can be rapid (IPCC 2012). Consequently, rapid broad-scale changes in ecosystems are of increasing concern. Climate change can directly affect species physiology, phenology, and distribution, as highlighted throughout this book (e.g. Citations to chapters in this volume to be added). Changes in one species can also affect other species (Cahill et al. 2013), particularly when dominant or co-dominant species that collectively provide habitat for other species are impacted (e.g. tree canopy architecture in many forest ecosystems; coral species via their reefs). Several rapid ecological changes have occurred at spatial scales that are sufficiently broad enough to represent biome changes (Gonzalez et al. 2010, Settele et al. 2014; Fig. 1 A). Rapid broad-scale changes differ from other patterns of vegetation dynamics in that they result in a “crash” in one or more populations (Breshears et al. 2008) over large areas of the affected region. Rapid broad-scale changes triggered by climate can include mega-fires; drought-triggered tree die-off and associated pest and pathogen outbreaks (Breshears et al. 2005, Safranyik et al. 2007); and hurricanes and wind-throw events (IPCC 2012, 2014). These rapid broad-scale changes can rapidly alter other factors such as resultant microclimate, which in turn can affect numerous other species and associated biodiversity (Royer et al. 2011; Fig. 1B). Many examples of broad-scale changes are documented in the paleoecology literature (Settele et al. 2014), although the temporal resolution at which those events can be resolved is relatively coarse (often centuries or longer). Such broad-scale changes documented in the paleoecology literature provide examples of types of change are likely to be of increasing concern in the future (Settele et al. 2014). Contemporary events have highlighted that broad-scale changes can occur rapidly (years or less; Breshears et al. 2005, Gonzalez et al. 2010, Settele et al. 2014). These rapid broad-scale changes will have important consequences for biodiversity beyond the direct impacts of climate change through the cascading effects associated with ecosystem structural and functional changes. The objective of this chapter is to alert readers to recent and projected rapid ecosystem changes and their potential consequences for biodiversity at ecosystem, landscape and regional scales.

Book chapter↗
USGS2022

It’s time for focused in situ studies of planetary surface-atmosphere interactions

A critical gap in planetary observations has been in situ characterization of extra-terrestrial, present-day atmospheric and surface environments and activity. While some surface activity has been observed and some in situ meteorological measurements have been collected by auxiliary instruments on Mars, existing information is insufficient to conclusively characterize the natural processes via concurrent and high-resolution measurement of environmental drivers and activity. Thus, many atmospheric, aeolian, and other surface processes models – which are used to generate key constraints on science and exploration in many areas of planetary investigation—such as surface exposure/erosion estimates, landscape interpretation, and modeling dust storm development—remain untested under non-Earth conditions. Analogous terrestrial processes are often studied intensively via numerical modeling that integrates empirical results from laboratory and/or field studies of process-response interactions between the atmosphere and relevant surface landforms. Incorporation of such in situ measurements into model development has significantly advanced our understanding of atmosphere-surface interactions and related geomorphic processes on Earth, and is poised to do so on other planets. However, to date, such testing and refinement have not been possible in other planetary environments, partially because investigations of this sort require new technologies, mission architectures, and operations designs (e.g., different from large rovers focused on geochemical investigations) to fully address the key gaps in our understanding while keeping cost and risk low. Fortunately, technological developments in the areas of surface access, instrumentation, and onboard processing/memory now enable small spacecraft to accommodate meteorological and aeolian instrumentation that could collect the needed measurements to fill this critical gap while remaining within typical small spacecraft resource budgets. Furthermore, maturity of our understanding of the broader geologic and atmospheric context on Mars provides a ready framework for ingestion of discrete ground truth measurements into our understanding of the broader and multi-scale martian natural systems and processes. These advancements make addressing key science questions with novel mission concepts feasible, promising results that would significantly advance our understanding of extraterrestrial surface-atmosphere interactions. This summary follows from a community-generated white paper for the ongoing Planetary Science/Astrobiology Decadal Survey, small spacecraft concept development at JPL, and numerous JPL and community discussions.

Conference Paper↗
USGS2025

Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets

Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.

Alabama. Mississippi↗
USGS2009

Implications of the fluvial history of the Wacheqsa River for hydrologic engineering and water use at Chavín de Húntar, Peru

Channeling of water through a variety of architectural features represents a significant engineering investment at the first millennium B.C. ceremonial center of Chavín de Huántar in the Peruvian Central Andes. The site contains extensive evidence of the manipulation of water, apparently for diverse purposes. The present configuration of the two local rivers, however, keeps available water approximately 9m below the highest level of water-bearing infrastructure in the site. Geomorphic and archaeological investigation of the fluvial history of the Wacheqsa River has revealed evidence that the Chavín-era configuration of the Wacheqsa River was different. A substantially higher water level, likely the result of a local impoundment of river water caused by a landslide dam, made the provision of water for the hydrologic system within the site a more readily practical possibility. We review what is known of that system and argue that the fluvial history of the Wacheqsa River is critical to understanding this aspect of hydrologic engineering and ritual practice at Chavín. This study demonstrates the relative rapidity and archaeological relevance of landscape change in a dynamic environment.

Chavin de Huantar↗
USGS2022

Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

Florida↗