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At least 811 records · Page 45Linked to original sources
USGS2009

Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

For two decades, remotely sensed data from imaging spectrometers have been used to estimate non-pigment biochemical constituents of vegetation, including water, nitrogen, cellulose, and lignin. This interest has been motivated by the important role that these substances play in physiological processes such as photosynthesis, their relationships with ecosystem processes such as litter decomposition and nutrient cycling, and their use in identifying key plant species and functional groups. This paper reviews three areas of research to improve the application of imaging spectrometers to quantify non-pigment biochemical constituents of plants. First, we examine recent empirical and modeling studies that have advanced our understanding of leaf and canopy reflectance spectra in relation to plant biochemistry. Next, we present recent examples of how spectroscopic remote sensing methods are applied to characterize vegetation canopies, communities and ecosystems. Third, we highlight the latest developments in using imaging spectrometer data to quantify net primary production (NPP) over large geographic areas. Finally, we discuss the major challenges in quantifying non-pigment biochemical constituents of plant canopies from remotely sensed spectra.

Remote Sensing of Environment↗
USGS2021

System characterization report on Resourcesat-2 Advanced Wide Field Sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that AWiFS has an interior geometric performance in the range of −16.080 (−0.268 pixel) to 35.520 meters (m; 0.592 pixel) in easting and −25.680 (−0.428 pixel) to 23.400 m (0.390 pixel) in northing in band-to-band registration, an exterior geometric error of −64.262 (−1.071 pixels) to −19.059 m (−0.318 pixel) in easting and −29.028 (−0.484 pixel) to 41.249 m (0.687 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 2.29–2.36 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.030–0.035.

Open-File Report↗
USGS2021

System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.

Open-File Report↗
USGS2020

QCam: sUAS-based doppler radar for measuring river discharge

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR © Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m 3 /s) (2.5%); 62.1 m 3 /s (−10.4%); 3.42 m 3 /s (7.3%), and 1579 m 3 /s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available.

Remote Sensing↗
USGS2006

Development and implementation of software systems for imaging spectroscopy

Specialized software systems have played a crucial role throughout the twenty-five year course of the development of the new technology of imaging spectroscopy, or hyperspectral remote sensing. By their very nature, hyperspectral data place unique and demanding requirements on the computer software used to visualize, analyze, process and interpret them. Often described as a marriage of the two technologies of reflectance spectroscopy and airborne/spaceborne remote sensing, imaging spectroscopy, in fact, produces data sets with unique qualities, unlike previous remote sensing or spectrometer data. Because of these unique spatial and spectral properties hyperspectral data are not readily processed or exploited with legacy software systems inherited from either of the two parent fields of study. This paper provides brief reviews of seven important software systems developed specifically for imaging spectroscopy.

Conference Paper↗
USGS2010

Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data

Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R2 of the organic layer depth reduction models was 0.60 and 0.55 (pb0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R2 to 0.80 and 0.79 (pb0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (N200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.

Remote Sensing of Environment↗
USGS2017

U.S. Geological Survey shrub/grass products provide new approach to shrubland monitoring

In the Western United States, shrubland ecosystems provide vital ecological, hydrological, biological, agricultural, and recreational services. However, disturbances such as livestock grazing, exotic species invasion, conversion to agriculture, climate change, urban expansion, and energy development are altering these ecosystems. Improving our understanding of how shrublands are distributed, where they are changing, the extent of the historical change, and likely future change directions is critical for successful management of these ecosystems. Remote-sensing technologies provide the most likely data source for large-area monitoring of ecosystem disturbance—both near-real time and historically. A monitoring framework supported by remote-sensing data can offer efficient and accurate analysis of change across a range of spatial and temporal scales. The U.S. Geological Survey has been working to develop new remote-sensing data, tools, and products to characterize and monitor these changing shrubland landscapes. Nine individual map products (components) have been developed that quantify the percent of shrub, sagebrush, big sagebrush, herbaceous, annual herbaceous, litter, bare ground, shrub height, and sagebrush height at 1-percent intervals in each 30-meter grid cell. These component products are designed to be combined and customized to widely support different applications in rangeland monitoring, analysis of wildlife habitat, resource inventory, adaptive management, and environmental review.

Fact Sheet↗
USGS2020

Inferring surface flow velocities in sediment-laden Alaskan rivers from optical image sequences acquired from a helicopter

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0 . 9 "> R 2 > 0 . 9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

Alaska↗
USGS2025

One-hundred fundamental, open questions to integrate methodological approaches in lake ice research

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real-time in situ monitoring, remote sensing, and modeling. The recent progress in low-power in situ sensing and data telemetry allows continuous tracing of under-ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process-based or statistical data-driven), with the recent technological advancements and integration of machine learning and hybrid process-based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less-studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross-pollination of intensive and extensive modes of study to broaden knowledge of the winter period.

Water Resources Research↗
USGS2025

Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements

Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere − critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (g sc ) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven g sc model; (3) a g sc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of g sc , finding that AET models with SIF-driven g sc models correlated well with in-situ measured AET (R 2 = 0.64). Modelled AET with dynamic variations of g sc generated lower prediction error (0.85 mm day −1 ), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.

Journal of Hydrology↗
USGS2020

Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails ( Rallus obsoletus yumanensis ) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.

Arizona, California, Nevada↗
USGS2003

A strategy for mapping mid-scale existing vegetation in support of national fire fuel assessment

Geospatial distribution of natural vegetation is among the very important environmental parameters required for applications ranging from global climate change to monitoring of natural hazards, monitoring of ecosystem vitality, and fire management practices. Increasingly sophisticated applications require vegetation datasets to cover large areas at a suitable scale and provide sufficiently detailed information. In this paper, we describe a research effort to develop a remote sensing methodology capable of producing 30-meter resolution, wall-to-wall coverage of existing vegetation types and structure variables in support of a multi-agency fire fuels and fire risks assessment project. Success of this remote sensing research effort is dependent on improved sensor and data qualities, a thorough understanding of regional and local vegetation ecology, successful integration of remote sensing with a large amount of field plot data, and flexible mapping algorithms. Preliminary results produced in the Wasatch Range and Uinta Mountains of central Utah include 28 vegetation types with an overall accuracy of 60% (average by life forms), percent canopy density (sub-pixel density) of forest, shrub, and herbaceous cover (correlation coefficient of 89, 60, and 55% respectively), and average top canopy height of forest, shrub, and herbaceous cover (correlation coefficient of 73, 50, 20% respectively). Techniques to improve the first-round results are discussed, including refinements of mapping models and use of relevant environmental gradients and potential vegetation classification associated with actual vegetation types.

Conference Paper↗
USGS2013

Use of Normalized Difference Vegetation Index (NDVI) habitat models to predict breeding birds on the San Pedro River, Arizona

Successful management practices of avian populations depend on understanding relationships between birds and their habitat, especially in rare habitats, such as riparian areas of the desert Southwest. Remote-sensing technology has become popular in habitat modeling, but most of these models focus on single species, leaving their applicability to understanding broader community structure and function largely untested. We investigated the usefulness of two Normalized Difference Vegetation Index (NDVI) habitat models to model avian abundance and species richness on the upper San Pedro River in southeastern Arizona. Although NDVI was positively correlated with our bird metrics, the amount of explained variation was low. We then investigated the addition of vegetation metrics and other remote-sensing metrics to improve our models. Although both vegetation metrics and remotely sensed metrics increased the power of our models, the overall explained variation was still low, suggesting that general avian community structure may be too complex for NDVI models.

Arizona↗
USGS2023

National Civil Applications Center

Introduction The U.S. Geological Survey (USGS) National Civil Applications Center (NCAC) analyzes remote-sensing data from the Intelligence Community (IC) and the U.S. Department of Defense (DOD) to support public safety missions and to study land-surface and environmental changes. The NCAC provides remotely sensed images to USGS scientists and other civilian Federal agencies; the images come from intelligence and military sensors, referred to as U.S. National Imagery Systems (USNIS), and unclassified commercial satellite data purchased by the DOD. Often these data are referred to as Geospatial Intelligence (GEOINT), which is defined in U.S. Code, title 10, section 467 as “the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on or about the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information.” The NCAC also provides the secretariat with staff and manages the U.S. interagency Civil Applications Committee (CAC), which oversees and facilitates the appropriate civilian uses of overhead remote-sensing technology and data collected by military and intelligence systems and commercial sources. Funded by the USGS National Land Imaging Program, the NCAC operates facilities in Reston, Virginia, and Lakewood, Colorado.

Fact Sheet↗
USGS2020

New operational national satellite burned area product

Introduction Lack of consistent spatial and temporal fire information with relevant spatial resolution hinders land management and broad-scale assessments of fire activity, especially in the eastern United States and the Great Plains where fi re is important ecologically and culturally. Remote sensing can be used to monitor fi re activity, augment existing fi re data, and fill information gaps. In particular, Landsat offers one of the most complete time series of remote sensing data sets as the Landsat satellites with spectral bands useful for mapping fi res and burn severity have been operational since 1984. Furthermore, Landsat satellite imagery collect data at a resolution useful for on-the-ground comparisons and management decisions. Methods A gradient-boosting regression model algorithm was used to predict burn probabilities (BP), indicating the likelihood that a pixel had burned in a fire. Then the algorithm translated the burn probability images to burn classification (BC) images using thresholding and region growing. Burned areas smaller than 5 acres were removed to reduce noise. The BP and BC products were generated for Landsat scenes collected from 1984 to present with <= 80% cloud cover and are available for download from USGSs EarthExplorer website (www.earthexplorer. usgs.gov). Annual summaries were also produced from the scene-level BC and BP products. These include: 1) the maximum burn probability value from all scenes in a year, (2) the burn classification count, or the number of times a pixel was classified as burned in a year, (3) the burn date, or the day of year of the first Landsat scene a pixel was classified as burned, and (4) a burn classification which removes burned areas still visible from previous years with a filter. Results The Landsat Burned Area Products identified 183% more burned area than the Monitoring Trends in Burn Severity and 56% more burned area than the MODIS MCD64A1.006 products2. From 1984 through 2018, annual burned area mapped by the Landsat Burned Area Products averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. The Landsat Burned Area products consistently map burned areas 5 acres or larger over time while capturing unburned islands within fi res. Such characteristics are critical for understanding long-term impacts of fi res on human and natural systems. The products are being routinely produced as new Landsat data are collected and demonstrate the utility remote sensing offers for monitoring landscape change.

Report↗
USGS2019

A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Harmful algal blooms (HABs) have been increasing in intensity across many waterbodies worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework to improve estimates of HAB timing, extent, and intensity using five independent sets of chlorophyll a (chl-a) data sampled from June to October, 2008 to 2017. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results indicate the model can estimate daily, location-specific chl-a concentrations with reasonable accuracy (R2 = 55%) between monitoring cruises. Conditional simulations provide probabilistic estimates of algal biomass and surface areal extent, which are compared to remote sensing estimates. The simulations also provide, for the first time, comprehensive estimates of overall bloom biomass based on depth-integrated concentrations, with quantified uncertainties. These estimates enhance our understanding of HAB variability and can inform HAB monitoring network design, predictive modeling, and management.

Lake Erie↗
USGS2016

The plant phenology monitoring design for the National Ecological Observatory Network

Phenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability or day length are of particular interest. This article provides an overview of the plant phenology sampling which will be conducted by the U.S. National Ecological Observatory Network NEON, the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30-year life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower-based cameras, satellite remote sensing and annual high-resolution airborne remote sensing. Ground-based measurements can be used to calibrate and improve satellite-derived phenometrics. NEON&rsquo;s phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continentalscale inference about the status, trends, causes and ecological consequences of phenological change.

Ecosphere↗
USGS2008

Data management and digital delivery of analog data

The U.S. Geological Survey's (USGS) data archive at the Earth Resources Observation and Science (EROS) Center is a comprehensive and impartial record of the Earth's changing land surface. USGS/EROS has been archiving and preserving land remote sensing data for over 35 years. This remote sensing archive continues to grow as aircraft and satellites acquire more imagery. As a world leader in preserving data, USGS/EROS has a reputation as a technological innovator in solving challenges and ensuring that access to these collections is available. Other agencies also call on the USGS to consider their collections for long-term archive support. To improve access to the USGS film archive, each frame on every roll of film is being digitized by automated high performance digital camera systems. The system robotically captures a digital image from each film frame for the creation of browse and medium resolution image files. Single frame metadata records are also created to improve access that otherwise involves interpreting flight indexes. USGS/EROS is responsible for over 8.6 million frames of aerial photographs and 27.7 million satellite images.

Conference Paper↗