Geology topics

Jennifer R. Rover

Publications and source records attributed to Jennifer R. Rover.

16 recordsLinked to original sources

USGS2020

Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach

Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).

Remote Sensing of Environment↗

USGS2018

Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region

To improve understanding of the distribution of ecologically important, ephemeral wetland habitats across the Great Plains, the occurrence and distribution of surface water in playa wetland complexes were documented for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. This information is important because it informs land and wildlife managers about the timing and location of habitat availability. Data with an accurate timestamp that indicate the presence of water, the percent of the area inundated with water, and the spatial distribution of playa wetlands with water are needed for a host of resource inventory, monitoring, and research applications. For example, the distribution of inundated wetlands forms the spatial pattern of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface waters that recharge the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Documentation of these spatially and temporally intricate processes, here, provides data required to assess connections between inundation and multiple environmental drivers, such as climate, land use, soil, and topography. Climate drivers are understood to interact with land cover, land use and soil attributes in determining the amount of water that flows overland into playa wetlands. Results indicated significant spatial variability represented by differences in the percent of playas inundated among States within the GPLCC. Further, analysis-of-variance comparison of differences in inundation between years showed significant differences in all cases. Although some connections with seasonal moisture patterns may be observed, the complex spatial-temporal gradients of precipitation, temperature, soils, and land use need to be combined as covariates in multivariate models to effectively account for these patterns. We demonstrate the feasibility of using classification of Landsat satellite imagery to describe playa-wetland inundation across years and seasons. Evaluating classifications representing only 4 years of imagery, we found significant year-to-year and state-to-state differences in inundation rates.

Great Plains Landscape Conservation Cooperative↗

USGS2016

Trophic dynamics of shrinking Subarctic lakes: naturally eutrophic waters impart resilience to rising nutrient and major ion concentrations

Shrinking lakes were recently observed for several Arctic and Subarctic regions due to increased evaporation and permafrost degradation. Along with lake drawdown, these processes often boost aquatic chemical concentrations, potentially impacting trophic dynamics. In particular, elevated chemical levels may impact primary productivity, which may in turn influence populations of primary and secondary consumers. We examined trophic dynamics of 18 shrinking lakes of the Yukon Flats, Alaska, that had experienced pronounced increases in nutrient (>200 % total nitrogen, >100 % total phosphorus) and ion concentrations (>100 % for four major ions combined) from 1985-1989 to 2010-2012, versus 37 stable lakes with relatively little chemical change over the same period. We found that phytoplankton stocks, as indexed by chlorophyll concentrations, remained unchanged in both shrinking and stable lakes from the 1980s to 2010s. Moving up the trophic ladder, we found significant changes in invertebrate abundance across decades, including decreased abundance of five of six groups examined. However, these decadal losses in invertebrate abundance were not limited to shrinking lakes, occurring in lakes with stable surface areas as well. At the top of the food web, we observed that probabilities of lake occupancy for ten waterbird species, including adults and chicks, remained unchanged from the period 1985-1989 to 2010-2012. Overall, our study lakes displayed a high degree of resilience to multi-trophic cascades caused by rising chemical concentrations. This resilience was likely due to their naturally high fertility, such that further nutrient inputs had little impact on waters already near peak production.

Alaska↗

USGS2015

Effect of permafrost thaw on the dynamics of lakes recharged by ice-jam floods: case study in Yukon Flats, Alaska

Large river floods are a key water source for many lakes in fluvial periglacial settings. Where permeable sediments occur, the distribution of permafrost may play an important role in the routing of floodwaters across a floodplain. This relationship is explored for lakes in the discontinuous permafrost of Yukon Flats, interior Alaska, using an analysis that integrates satellite-derived gradients in water surface elevation, knowledge of hydrogeology, and hydrologic modeling. We observed gradients in water surface elevation between neighboring lakes ranging from 0.001 to 0.004. These high gradients, despite a ubiquitous layer of continuous shallow gravel across the flats, are consistent with limited groundwater flow across lake basins resulting from the presence of permafrost. Permafrost impedes the propagation of floodwaters in the shallow subsurface and constrains transmission to “fill-and-spill” over topographic depressions (surface sills), as we observed for the Twelvemile-Buddy Lake pair following a May 2013 ice-jam flood on the Yukon River. Model results indicate that permafrost table deepening of 1–11 m in gravel, depending on watershed geometry and subsurface properties, could shift important routing of floodwater to lakes from overland flow (fill-and-spill) to shallow groundwater flow (“fill-and-seep”). Such a shift is possible in the next several hundred years of ground surface warming, and may bring about more synchronous water level changes between neighboring lakes following large flood events. This relationship offers a potentially useful tool, well-suited to remote sensing, for identifying long-term changes in shallow groundwater flow resulting from thawing of permafrost.

Alaska↗

USGS2015

Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska

Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha −1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha −1 (relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

Alaska↗

USGS2015

Mapping wetlands and surface water in the Prairie Pothole Region of North America: Chapter 16

The Prairie Pothole Region (PPR) is one of the most highly productive wetland regions in the world. Prairie Pothole wetlands serve as a primary feeding and breeding habitat for more than one-half of North America’s waterfowl population, as well as a variety of songbirds, waterbirds, shorebirds, and other wildlife. During the last century, extensive land conversions from grassland with wetlands to cultivated cropland and grazed pastureland segmented and reduced wetland habitat. Inventorying and characterizing remaining wetland habitat is critical for the management of wetland ecosystem services. Remote sensing technologies are often utilized for mapping and monitoring wetlands. This chapter presents background specific to the PPR and discusses approaches employed in mapping its wetlands before presenting a case study.

Prairie Pothole Region↗

USGS2014

Pronounced chemical response of Subarctic lakes to climate-driven losses in surface area

Losses in lake area have been observed for several Arctic and Subarctic regions in recent decades, with unknown consequences for lake ecosystems. These reductions are primarily attributed to two climate-sensitive mechanisms, both of which may also cause changes in water chemistry: (i) increased imbalance of evaporation relative to inflow, whereby increased evaporation and decreased inflow act to concentrate solutes into smaller volumes; and (ii) accelerated permafrost degradation, which enhances sublacustrine drainage while simultaneously leaching previously frozen solutes into lakes. We documented changes in nutrients [total nitrogen (TN), total phosphorus (TP)] and ions (calcium, chloride, magnesium, sodium) over a 25 year interval in shrinking, stable, and expanding Subarctic lakes of the Yukon Flats, Alaska. Concentrations of all six solutes increased in shrinking lakes from 1985–1989 to 2010–2012, while simultaneously undergoing little change in stable or expanding lakes. This created a present-day pattern, much weaker or absent in the 1980s, in which shrinking lakes had higher solute concentrations than their stable or expanding counterparts. An imbalanced evaporation-to-inflow ratio (E/I) was the most likely mechanism behind such changes; all four ions, which behave semiconservatively and are prone to evapoconcentration, increased in shrinking lakes and, along with TN and TP, were positively related to isotopically derived E/I estimates. Moreover, the most conservative ion, chloride, increased >500% in shrinking lakes. Conversely, only TP concentration was related to probability of permafrost presence, being highest at intermediate probabilities. Overall, the substantial increases of nutrients (TN >200%, TP >100%) and ions (>100%) may shift shrinking lakes towards overly eutrophic or saline states, with potentially severe consequences for ecosystems of northern lakes.

Alaska↗

USGS2014

Effects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest

A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000–2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30–40 years following fire, and deciduous stands generally have higher EPA than coniferous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.

Alaska↗

USGS2014

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2013

Controls on recent Alaskan lake changes identified from water isotopes and remote sensing

High-latitude lakes are important for terrestrial carbon dynamics and waterfowl habitat driving a need to better understand controls on lake area changes. To identify the existence and cause of recent lake area changes in the Yukon Flats, a region of discontinuous permafrost in north central Alaska, we evaluate remotely sensed imagery with lake water isotope compositions and hydroclimatic parameters. Isotope compositions indicate that mixtures of precipitation, river water, and groundwater source ~95% of the studied lakes. The remaining minority are more dominantly sourced by snowmelt and/or permafrost thaw. Isotope-based water balance estimates indicate 58% of lakes lose more than half of inflow by evaporation. For 26% of the lakes studied, evaporative losses exceeded supply. Surface area trend analysis indicates that most lakes were near their maximum extent in the early 1980s during a relatively cool and wet period. Subsequent reductions can be explained by moisture deficits and greater evaporation.

Alaska↗

USGS2012

MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components

Alaska↗

USGS2012

Carbon dioxide and methane emissions from the Yukon River system

Carbon dioxide (CO 2 ) and methane (CH 4 ) emissions are important, but poorly quantified, components of riverine carbon (C) budgets. This is largely because the data needed for gas flux calculations are sparse and are spatially and temporally variable. Additionally, the importance of C gas emissions relative to lateral C exports is not well known because gaseous and aqueous fluxes are not commonly measured on the same rivers. We couple measurements of aqueous CO 2 and CH 4 partial pressures ( p CO 2 , p CH 4 ) and flux across the water-air interface with gas transfer models to calculate subbasin distributions of gas flux density. We then combine those flux densities with remote and direct observations of stream and river water surface area and ice duration, to calculate C gas emissions from flowing waters throughout the Yukon River basin. CO 2 emissions were 7.68 Tg C yr −1 (95% CI: 5.84 −10.46), averaging 750 g C m −2 yr −1 normalized to water surface area, and 9.0 g C m −2 yr −1 normalized to river basin area. River CH 4 emissions totaled 55 Gg C yr −1 or 0.7% of the total mass of C emitted as CO 2 plus CH 4 and ∼6.4% of their combined radiative forcing. When combined with lateral inorganic plus organic C exports to below head of tide, C gas emissions comprised 50% of total C exported by the Yukon River and its tributaries. River CO 2 and CH 4 derive from multiple sources, including groundwater, surface water runoff, carbonate equilibrium reactions, and benthic and water column microbial processing of organic C. The exact role of each of these processes is not yet quantified in the overall river C budget.

Yukon River Basin↗

USGS2012

Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8 Mg/ha and a mean bias error of 5.2 Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R 2 = 0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10 Mg/ha to 134 Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion.

Alaska↗

USGS2011

Establishing water body areal extent trends in interior Alaska from multi-temporal Landsat data

An accurate approach is needed for monitoring, quantifying and understanding surface water variability due to climate change. Separating inter- and intra-annual variances from longer-term shifts in surface water extents due to contemporary climate warming requires repeat measurements spanning a several-decade period. Here, we show that trends developed from multi-date measurements of the extents of more than 15,000 water bodies in central Alaska using Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data (1979–2009) were highly influenced by the quantity and timing of the data. Over the 30-year period from 1979 to 2009, the study area had a net decrease ( p < 0.05) in the extents of 3.4% of water bodies whereas 86% of water bodies exhibited no significant change. The Landsat-derived dataset provides an opportunity for additional research assessing the drivers of lake and wetland change in this region.

Alaska↗

USGS2011

On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)

The spectral vegetation index (ρNIR – ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI), and normalized burn ratio (NBR), etc. After reviewing each term’s definition, associated sensors, and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3 µm, 1.55–1.75 µm, or 2.05–2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term’s popularity, and the “rule of priority” in scientific nomenclature, NDWI, NDII, and NBR, each corresponding to the three SWIR regions, are more preferable terms.

International Journal of Remote Sensing↗

USGS2010

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

International Journal of Remote Sensing↗