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USGS2022

Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption

Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.

Alaska↗

USGS2014

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Idaho, Utah, Wyoming↗

USGS2017

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html

International Journal of Digital Earth↗

USGS2018

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

Cuenca Los Ojos, San Bernardino National Wildlife ↗

USGS2024

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Sardon catchment↗

USGS2015

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation ( R 2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships ( R 2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation ( R 2 ) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

International Journal of Applied Earth Observation↗

USGS2015

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover ( r 2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass ( r 2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

Maryland↗

USGS2010

Hyperspectral analysis of columbia spotted frog habitat

Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96 (44 wetlands) correctly classified with the 2002 data and 89 (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74 (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72 (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats. ?? 2010 The Wildlife Society.

Journal of Wildlife Management↗

USGS2018

Assessment of Alaska rain-on-snow events using dynamical downscaling

The ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

Alaska↗

USGS1978

EROS Data Center Landsat digital enhancement techniques and imagery availability

The US Geological Survey's EROS Data Center (EDC) is experimenting with the production of digitally enhanced Landsat imagery. Advanced digital image processing techniques are used to perform geometric and radiometric corrections and to perform contrast and edge enhancements. The enhanced image product is produced from digitally preprocessed Landsat computer compatible tapes (CCTs) on a laser beam film recording system. Landsat CCT data have several geometric distortions which are corrected when NASA produces the standard film products. When producing film images from CCT's, geometric correction of the data is required. The EDC Digital Image Enhancement System (EDIES) compensates for geometric distortions introduced by Earth's rotation, variable line length, non-uniform mirror scan velocity, and detector misregistration. Radiometric anomalies such as bad data lines and striping are common to many Landsat film products and are also in the CCT data. Bad data lines or line segments with more than 150 contiguous bad pixels are corrected by inserting data from the previous line in place of the bad data. Striping, caused by variations in detector gain and offset, is removed with a destriping algorithm applied after digitally enhancing the data. Image enhancement is performed by applying a linear contrast stretch and an edge enhancement algorithm. The linear contrast enhancement algorithm is designed to expand digitally the full range of useful data recorded on the CCT over the range of 256 digital counts. This minimizes the effect of atmospheric scattering and saturates the relative brightness of highly reflecting features such as clouds or snow. It is the intent that no meaningful terrain data are eliminated by the digital processing. The edge enhancement algorithm is designed to enhance boundaries between terrain features that exhibit subtle differences in brightness values along edges of features. After the digital data have been processed, data for each Landsat band are recorded on black-and-white film with a laser beam film recorder (LBR). The LBR corrects for aspect ratio distortions as the digital data are recorded on the recording film over a preselected density range. Positive transparencies of MSS bands 4, 5, and 7 produced by the LBR are used to make color composite transparencies. Color film positives are made photographically from first generation black-and-white products generated on the LBR.

Canadian Journal of Remote Sensing↗

USGS2014

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs.

California;Nevada↗

USGS2013

The stability of sulfate and hydrated sulfate minerals near ambient conditions and their significance in environmental and planetary sciences

Sulfate and hydrated sulfate minerals are abundant and ubiquitous on the surface of the Earth and also on other planets and their satellites. The humidity-buffer technique has been applied to study the stability of some of these minerals at 0.1 MPa in terms of temperature-relative humidity space on the basis of hydration-dehydration reversal experiments. Updated phase relations in the binary system MgSO 4 -H 2 O are presented, as an example, to show how reliable thermodynamic data for these minerals could be obtained based on these experimental results and thermodynamic principles. This approach has been applied to sulfate and hydrated sulfate minerals of other metals, including Fe (both ferrous and ferric), Zn, Ni, Co, Cd, and Cu. Metal-sulfate salts play important roles in the cycling of metals and sulfate in terrestrial systems, and the number of phases extends well beyond the simple sulfate salts that have thus far been investigated experimentally. The oxidation of sulfide minerals, particularly pyrite, is a common process that initiates the formation of efflorescent metal-sulfate minerals. Also, the overall abundance of iron-bearing sulfate salts in nature reflects the fact that the weathering of pyrite or pyrrhotite is the ultimate source for many of these phases. Many aspects of their environmental significance are reviewed, particularly in acute effects to aquatic ecosystems related to the dissolution of sulfate salts during rain storms or snow-melt events. Hydrous Mg, Ca, and Fe sulfates were identified on Mars, with wide distribution and very large quantities at many locations, on the basis of spectroscopic observations from orbital remote sensing and surface explorations by rovers. However, many of these findings do not reveal the detailed information on the degree of hydration that is essential for rigorous interpretation of the hydrologic history of Mars. Laboratory experiments on stability fields, reactions pathways, and reaction rates of hydrous sulfates likely to be found on Mars enhance our understanding of the degrees of hydration of various sulfates that should currently exist on Mars at various seasons and locations and during various atmospheric pressure and obliquity periods. Two sets of systematic experiments were described; one on hydrous Mg sulfates and the other on hydrous Fe 3+ sulfates. Also, their implications to Mars sulfates mineralogy were discussed.

Journal of Asian Earth Sciences↗

USGS2023

Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub

Objective A central challenge in applied ecology is understanding how organisms are spatially and temporally distributed and how management might be tailored to maintain or restore species distributions. The niche concept is central to understanding species distributions, but the diversity of niche definitions requires that multiple dimensions be considered. For example, the Grinnellian niche concept focuses on environmental conditions that allow species to persist, the Eltonian niche concept stresses the influence of biotic interactions, and the fundamental niche concept considers both abiotic and biotic environmental features to define spaces that organisms could occupy. Methods We combined abiotic ( A ), biotic ( B ), and movement ( M ) information (collectively, BAM model) to map the multidimensional niche of Prairie Chub Macrhybopsis australis , a regionally endemic freshwater fish currently under review for listing under the Endangered Species Act. We estimated A using remotely sensed environmental riverscape variables, B using the spatial distribution of a hybridization zone between Prairie Chub and Shoal Chub M. hyostoma , and M using data from a mark–recapture study. Result The BAM model estimated the spatial extent of multiple niches, including the Grinnellian ( A ; extent = 944 km of river), Eltonian ( B ; 2974 km), and fundamental niche (overlap of A + B ; 645 km) niches. When A , B , and M components were combined, the estimated extent of the Prairie Chub niche was 645 km. Conclusion Our work shows that the realized, multidimensional niche of Prairie Chub includes medium to large rivers with high habitat connectivity in the upper–middle Red River basin upstream of the distribution of Shoal Chub. The current Prairie Chub distribution could be maintained by preventing further habitat fragmentation and maintaining the environmental gradient separating Prairie Chub from Shoal Chub. Expansion of the species distribution may be possible through restoration of longitudinal fluvial connectivity.

North American Journal of Fisheries Management↗

USGS2021

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ET a ), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET a , and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ET a yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ET a estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (r ah ) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.

Urucuia Aquifer System↗

USGS2003

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Journal of Geophysical Research E: Planets↗

USGS2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Conterminous United States↗

USGS2021

Northern bobwhite occupancy patterns on multiple spatial scales across Arkansas

Northern bobwhite Colinus virginianus populations have been rapidly declining in the eastern, central, and southern United States for decades. Land use change and an incompatibility between northern bobwhite resource needs and human land use practices have driven declines. Here, we applied occupancy analyses on two spatial scales (state level and ecoregion level) to more than 5,000 northern bobwhite surveys conducted over 6 y across the entire state of Arkansas to explore patterns in occupancy and land use variables, and to identify priority areas for management and conservation. At the state level, northern bobwhite occupied 29% of sites and northern bobwhite were most likely to occur in areas with a high percentage of early successional habitat (grassland, pasture, and shrubland). The statewide model predicted that northern bobwhite were likely to occur (≥ 75% predicted occupancy) in < 20% of the state. Arkansas is comprised of five distinct ecoregions, and analyses at the ecoregion spatial scale showed that habitat associations of northern bobwhite could vary between ecoregions. For example, early successional habitat best predicted northern bobwhite occupancy in both the Arkansas River Valley and Ozark Mountains ecoregions, and other habitat associations such as the proportion of herbaceous habitat and hay-pasture habitat, respectively, further refined predictions. Contrastingly, richness of land cover classes alone best predicted northern bobwhite occupancy in the Ouachita Mountains ecoregion. Ecoregion-level models were thus more discerning than the state-level model and should be more helpful to managers in identifying priority conservation areas. However, in two of five ecoregions, surveys too rarely encountered northern bobwhite to accurately predict their occurrence. We found that likely occupied northern bobwhite habitat lay primarily on private properties (95%), but that numerous public entities own and manage land identified as suitable or likely occupied. We conclude that management of northern bobwhite in Arkansas could benefit from cooperation among state, federal, and military partners, as well as surrounding private landowners and that ecoregion-specific models may be more useful in identifying priority areas for management. Our approach incorporates multiple landscape scales when using remote sensing technology in conjunction with monitoring data and could have important application for the management of northern bobwhite and other grassland bird species.

Arkansas↗

USGS2015

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic C- H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf ( r 2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf ( r 2 = 0.79, RMSE = 2.1%, n = 56), and canopy ( r 2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).

International Journal of Applied Earth Observation↗