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This forty papers in this special issue focus on how intercalibration and comparison between sensors can provide an effective and convenient means of verifying their postlaunch performance and correcting their measurement differences.
1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status. 2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. 3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces ; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan. 4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures.
Whether a waterway is temporary or permanent influences regulatory protection guidelines, however, classification can be subjective due to a combination of factors, including time of year, antecedent moisture conditions, and previous experience of the field investigator. Our objective was to develop a standardized protocol using publicly available spatial information to classify ephemeral, intermittent, and perennial streams. Our hypothesis was that field observations of flow along the stream channel could be compared to results from a hydrologic model, providing an objective method of how these stream reaches can be identified. Flow-state sensors were placed at ephemeral, intermittent, and perennial stream reaches from May to December 2011 in the Appalachian coal basin of eastern Kentucky. This observed flow record was then used to calibrate the simulated saturation deficit in each channel reach based on the topographic wetness index used by TOPMODEL. Saturation deficit values were categorized as flow or no-flow days, and the simulated record of streamflow was compared to the observed record. The hydrologic model was more accurate for simulating flow during the spring and fall seasons. However, the model effectively identified stream reaches as intermittent and perennial in each of the two basins.
The U.S. Geological Survey's New Jersey and Iowa Water Science Centers deployed ultraviolet-visible spectrophotometric sensors at water-quality monitoring sites on the Passaic and Pompton Rivers at Two Bridges, New Jersey, on Toms River at Toms River, New Jersey, and on the North Raccoon River near Jefferson, Iowa to continuously measure in-stream nitrate plus nitrite as nitrogen (NO 3 + NO 2 ) concentrations in conjunction with continuous stream flow measurements. Statistical analysis of NO 3 + NO 2 vs . stream discharge during storm events found statistically significant links between land use types and sampling site with the normalized area and rotational direction of NO 3 + NO 2 -stream discharge (N-Q) hysteresis patterns. Statistically significant relations were also found between the normalized area of a hysteresis pattern and several flow parameters as well as the normalized area adjusted for rotational direction and minimum NO 3 + NO 2 concentrations. The mean normalized hysteresis area for forested land use was smaller than that of urban and agricultural land uses. The hysteresis rotational direction of the agricultural land use was opposite of that of the urban and undeveloped land uses. An r 2 of 0.81 for the relation between the minimum normalized NO 3 + NO 2 concentration during a storm vs . the normalized NO 3 + NO 2 concentration at peak flow suggested that dilution was the dominant process controlling NO 3 + NO 2 concentrations over the course of most storm events.
A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.
Groundwater-flow models are often calibrated using a limited number of observations relative to the unknown inputs required for the model. This is especially true for models that simulate groundwater surface-water interactions. In this case, subsurface temperature sensors can be an efficient means for collecting long-term data that capture the transient nature of physical processes such as seepage losses. Continuous and spatially dense network of diverse observation data can be used to improve knowledge of important physical drivers, conceptualize and calibrate variably saturated groundwater flow models. An example is presented for which the results of such analysis were used to help guide irrigation districts and water management decisions on costly upgrades to conveyance systems to improve water usage, farm productivity and restoration efforts to improve downstream water quality and ecosystems.
Thick, heterogeneous unsaturated zones away from large streams in desert areas have not previously been considered suitable for artificial recharge from ponds. To test the potential for recharge in these settings, 1.3 x 10 6 m 3 of water was infiltrated through a 0.36-ha pond along Oro Grande Wash near Victorville, California, between October 2002 and January 2006. The pond overlies a regional pumping depression 117 m below land surface and is located where thickness and permeability of unsaturated deposits allowed infiltration and saturated alluvial deposits were sufficiently permeable to allow recovery of water. Because large changes in water levels caused by nearby pumping would obscure arrival of water at the water table, downward movement of water was measured using sensors in the unsaturated zone. The downward rate of water movement was initially as high as 6 m/d and decreased with depth to 0.07 m/d; the initial time to reach the water table was 3 years. After the unsaturated zone was wetted, water reached the water table in 1 year. Soluble salts and nitrate moved readily with the infiltrated water, whereas arsenic and chromium were less mobile. Numerical simulations done using the computer program TOUGH2 duplicated the downward rate of water movement, accumulation of water on perched zones, and its arrival at the water table. Assuming 10 3 106 m3 of recharge annually for 20 years, a regional ground water flow model predicted water level rises of 30 m beneath the ponds, and rises exceeding 3m in most wells serving the nearby urban area.
Little is known about hydrogeochemical conditions beneath thick permafrost, particularly in fractured crystalline rock, due to difficulty in accessing this environment. The purpose of this investigation was to develop methods to obtain physical, chemical, and microbial information about the subpermafrost environment from a surface-drilled borehole. Using a U-tube, gas and water samples were collected, along with temperature, pressure, and hydraulic conductivity measurements, 420 m below ground surface, within a 535 m long, angled borehole at High Lake, Nunavut, Canada, in an area with 460-m-thick permafrost. Piezometric head was well above the base of the permafrost, near land surface. Initial water samples were contaminated with drill fluid, with later samples <40% drill fluid. The salinity of the non-drill fluid component was <20,000 mg/L, had a Ca/Na ratio above 1, with ??18O values ???5??? lower than the local surface water. The fluid isotopic composition was affected by the permafrost-formation process. Nonbacteriogenic CH4 was present and the sample location was within methane hydrate stability field. Sampling lines froze before uncontaminated samples from the subpermafrost environment could be obtained, yet the available time to obtain water samples was extended compared to previous studies. Temperature measurements collected from a distributed temperature sensor indicated that this issue can be overcome easily in the future. The lack of methanogenic CH4 is consistent with the high sulfate concentrations observed in cores. The combined surface-drilled borehole/U-tube approach can provide a large amount of physical, chemical, and microbial data from the subpermafrost environment with few, controllable, sources of contamination. ?? 2010 The Author(s). Journal compilation ?? 2010 National Ground Water Association.
The increasing availability of multi-scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model-assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the "hot" and "cold" reference conditions. The SSEBop model was used for computing ET for 12 years (2000-2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000-2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.
State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics ( κ ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.
Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.
Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.
The Landsat 8 satellite was launched on February 11, 2013, to systematically collect multispectral images for detection and quantitative analysis of changes on the Earth’s surface. The collected data are stored at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and continue the longest archive of medium resolution Earth images. There are two imaging instruments onboard the satellite: the Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS). This paper summarizes radiometric performance of the OLI including the bias stability, the system noise, saturation and other artifacts observed in its data during the first 1.5 years on orbit. Detector noise levels remain low and Signal-To-Noise Ratio high, largely exceeding the requirements. Impulse noise and saturation are present in imagery, but have negligible effect on Landsat 8 products. Oversaturation happens occasionally, but the affected detectors quickly restore their nominal responsivity. Overall, the OLI performs very well on orbit and provides high quality products to the user community. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hyperspectral imaging (HSI) is an exciting and rapidly expanding area of instruments and technology in passive remote sensing. Due to quickly changing applications, the instruments are evolving to suit new uses and there is a need for consistent definition, testing, characterization and calibration. This paper seeks to outline a broad prescription and recommendations for basic specification, testing and characterization that must be done on Visible Near Infra-Red grating-based sensors in order to provide calibrated absolute output and performance or at least relative performance that will suit the user’s task. The primary goal of this paper is to provide awareness of the issues with performance of this technology and make recommendations towards standards and protocols that could be used for further efforts in emerging procedures for national laboratory and standards groups.
The Thermal Infrared Sensor (TIRS) instrument is the thermal-band imager on the Landsat-8 platform. The initial onorbit calibration estimates of the two TIRS spectral bands indicated large average radiometric calibration errors, -0.29 and -0.51 W/m 2 sr μm or -2.1K and -4.4K at 300K in Bands 10 and 11, respectively, as well as high variability in the errors, 0.87K and 1.67K (1-σ), respectively. The average error was corrected in operational processing in January 2014, though, this adjustment did not improve the variability. The source of the variability was determined to be stray light from far outside the field of view of the telescope. An algorithm for modeling the stray light effect was developed and implemented in the Landsat-8 processing system in February 2017. The new process has improved the overall calibration of the two TIRS bands, reducing the residual variability in the calibration from 0.87K to 0.51K at 300K for Band 10 and from 1.67K to 0.84K at 300K for Band 11. There are residual average lifetime bias errors in each band: 0.04 W/m 2 sr μm (0.30K) and -0.04 W/m 2 sr μm (-0.29K), for Bands 10 and 11, respectively.
Landsat 9 is currently undergoing testing at the integrated observatory level in preparation for launch from Vandenberg Air Force Base in 2021. Landsat 9 will replace Landsat 7 in orbit, 8 days out of phase with Landsat 8. Landsat 9 is largely a copy of Landsat 8 in terms of instrumentation, with an Operational Land Imager (OLI), model #2 and a Thermal Infrared Sensor (TIRS), model #2. The TIRS-2 is more significantly changed from TIRS with increased redundancy, as well as changes to the telescope baffling to improve stray light control and a revised scene select mirror encoder mechanism. Data quality of the Landsat 9 instruments is comparable to, or better than the Landsat 8 ones, with an increase to 14 bits of data transmitted and more detailed pre-launch characterization for OLI-2, and with more detailed characterization of the TIRS-2 pre-launch, in addition to the improved stray light control. The performance of the two instruments is summarized and compared to that of the Landsat 8 instruments.
The primary objective of the Earth Resources Observation System (EROS) Data Center (EDC) Distributed Active Archive Center (DAAC) for land processes data is to promote the interdisciplinary study and understanding of the integrated Earth system by providing remotely sensed and related ancillary data for the study, characterization, and monitoring of natural and anthropogenic conditions and processes existing and operating at or near the land surface. Generating, distributing, and preserving data and products from the National Aeronautics and Space Administration's (NASA) earth observing system (EOS) are primary functions of the DAAC. To that end, the EDC DAAC is using existing data sets in developing capabilities to efficiently and effectively ingest, process, manage, distribute, and archive for future generations land-related data collected by EOS sensors. Capabilities also are being developed to help users search for and acquire data and products, as well as to support their scientific application of those data and products. EDC DAAC programs and capability development activities address DAAC-defined science support requirements that relate to a broad spectrum of services and capabilities needed by science users.
The U.S. Geological Survey (USGS) has completed processing of the historical Landsat archive to Collection-2 as of December of 2020 and has released it to the public. As part of Collection-2, several geometric changes have been implemented, including changes to the ground control points (GCPs) and elevation datasets. These datasets are used as a geometric reference for all missions. In addition, mission specific improvements were included in Collection-2, such as improvements to the precision correction algorithms and updates of the calibration parameters. This paper discusses a preliminary analysis of a comparison between the Collection-1 and Collection-2 products of the entire Landsat archive. Compared to the Level 1 products in Collection-1, the number of Tier-1 precision- and terrain-corrected (L1TP) Level 1 products in Collection-2 increased by 6.26% across all sensors. Landsat 8 products showed an increase of Tier-1 L1TP products by 5.33%; Landsat 7 products showed an increase of Tier-1 products by 6.94%; and Landsat 5 and Landsat 4 Thematic Mapper (TM) products showed an increase of Tier-1 products by 9.35%. The geometric accuracy of the Tier-1 terrain corrected products also improved by 2 meters or more.