SEARCH · USGS Science

Results for “Remote Sensing Information”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42Linked to original sources

USGS2001

Geologic studies in Alaska by the U.S. Geological Survey, 1999

The collection of nine papers that follow continue the series of U.S. Geological Survey (USGS) investigative reports in Alaska under the broad umbrella of the geologic sciences. The series presents new and sometimes preliminary findings that are of interest to earth scientists in academia, government, and industry; to land and resource managers; and to the general public. Reports presented in Geologic Studies in Alaska cover a broad spectrum of topics from various parts of the State (fig. 1), serving to emphasize the diversity of USGS efforts to meet the Nation's needs for earth-science information in Alaska. The papers in this volume are organized under the topics: Hazards, Geologic Framework, Environment and Climate, and Resources. This organization is intended to reflect the scope and objectives of USGS geologic programs currently active in Alaska. The two Hazards studies discuss volcano-related topics in the seismically active southcentral Alaska region. The first paper revisits the eruptive events of Redoubt Volcano that occurred more than a decade ago and the subsequent development of the Alaska Volcano Observatory (AVO). This treatise documents the historic impact of this eruption and briefly summarizes the state of our knowledge of the other Cook Inlet, Alaska Peninsula, and Aleutian Island volcanoes. Finally, it discusses the recent role that AVO has had in seismic station installation and hazard assessment at volcanically active sites throughout the world. The second paper discusses the eruptive history of Snowy Mountain in the upper Alaska Peninsula. Because subsets of its 25-30 lava flows erupted as packages in short episodes, calculation of the volcano's lifetime average volumetric eruption rate is problematic. A portion of the cone was hydrothermally weakened and collapsed in the late Holocene producing a 22-km2 debris avalanche. Geologic Framework studies provide background information that is the scientific basis for present and future earth science investigations. The first paper compares and contrasts the Insular-Intermontane suture zone (IISZ) of southeast Alaska with the Adria-Europe suture zone (AESZ) of Switzerland and Hungary. The study develops the hypothesis that the zones have distinct differences as well as similarities and neither is a simple lithotectonic terrane boundary. The second paper discusses the relation among volcanic, glacial, and tectonic activity in the Cold Bay and False Pass 1 :250,000-scale quadrangles on the Alaska Peninsula. During Pleistocene time, continental-shelf glaciations and two massive volcanic centers were the dominant controls over landscape development. The third paper gives detailed geologic information for Paleozoic rocks within the Taylor Mountains D-1 quadrangle portion of the Holitna Lowland of southwestern Alaska. Because of the excellent preservation of megafossils, these Silurian and Ordovician strata lend themselves to detailed statigraphic investigations. Further, low thermal alteration indices of this area have made them a potential target of petroleum exploration. The final report in this section discusses the development of a new spectral enhancement approach for interpreting Multispectral Scanner (MSS) and Thematic Mapper (TM) satellite images. This technique enhances the use of remote sensing data in identifying geologic units in areas that have been poorly investigated. This study used this technique to better define the distribution of a JMtu (mafic, ultramafic, and sedimentary) unit and a PzZrqs (pelitic and quartzitic schist) unit. Environment and climate studies are the emphasis of two papers. One presents the first radiocarbon-dated postglacial vegetation history of the Kenai Mountains of southcentral Alaska. This reconstruction is the result of the analysis of pollen assemblages and peat from sediments collected in Tern Lake and presents a minimum age for deglaciation of these interior valleys at 9,31 0±200 yr B .P. Current vegetation, however, developed within the past ca. 2,500 years. A second study discusses the cycling of arsenic and cadmium in sub-arctic boreal forest ecosystems typical of interior Alaska and defines the importance of various natural (geogenic) sources. The transport and uptake into vegetation of these elements from soils developed from loess as well as soils developed from the major rock units is presented. The bioaccumulation of cadmium in willow (Salix sp.) and its potential consequence to the health of browsing animals is discussed. Papers related to resource issues comprise the topic of the final report. This paper presents a brief statistical summary of the geochemistry of rock samples collected in the east-central portion of the Eagle 1 :250,000-scale quadrangle. This study helps define the rock unit source of both resource- and environmental-based chemical elements of interest in the Fortymile mining district. Two bibliographies at the end of the volume list reports covering Alaska earth science topics in USGS publications during 1999 and reports about Alaska by USGS authors in non-USGS publications during the same period.

Alaska↗

USGS2025

Remote sensing of river discharge based on critical flow theory

Critical flow theory provides a physical foundation for inferring discharge from measurements of wavelength and channel width made from images. In rivers with hydraulically steep local slopes greater than ∼0.01, flow velocities are high and the Froude number F r (ratio of inertial to gravitational forces) can approach 1.0 (critical flow) or greater. Under these conditions, undular hydraulic jumps (UHJ's) can form as standing wave trains at slope transitions or constrictions. The presence of UHJ's indicates that mean F r ≈ 1, implying that the velocity and depth of the flow and the spacing of the waves are uniquely related to one another. Discharges estimated from 82 Google Earth images agreed closely with discharges recorded at gaging stations ( R 2 = 0.98), with a mean bias of 1% ± 11%. This approach could provide reliable discharge information in many fluvial environments where critical flow occurs, which tend to be underrepresented in gage networks

Geophysical Research Letters↗

USGS2024

Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series

Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5–10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective; (2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal; (3) constructing training samples of ponds with high representativeness; and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond Object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary watershed-level study sites in the United States and China. Our approach yielded high overall accuracy (>95 %) for all sites and highlighted the ability of Sentinel-1/2 imagery to accurately detect small ponds (0.1–1 ha) across diverse landscapes. The average producer's accuracy for small ponds at county-level sites improved by ∼45 % compared to that of all other products with a 10-m or higher spatial resolution, addressing the absence of such information in existing regional and global datasets. The generated county-level pond maps revealed the numerical dominance of ponds in lentic waters, their substantial area contribution in human-impacted regions, and the relevance of studying biogeochemical processes in smaller waterbodies.

Remote Sensing of Environment↗

USGS2020

Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty

Channels change in response to natural or anthropogenic fluctuations in streamflow and/or sediment supply and measurements of channel change are critical to many river management applications. Whereas repeated field surveys are costly and time‐consuming, remote sensing can be used to detect channel change at multiple temporal and spatial scales. Repeat images have been widely used to measure long‐term channel change, but these measurements are only significant if the magnitude of change exceeds the uncertainty. Existing methods for characterizing uncertainty have two important limitations. First, while the use of a spatially variable image co‐registration error avoids the assumption that errors are spatially uniform, this type of error, as originally formulated, can only be applied to linear channel adjustments, which provide less information on channel change than polygons of erosion and deposition. Second, previous methods use a level‐of‐detection (LoD) threshold to remove non‐significant measurements, which is problematic because real changes that occurred but were smaller than the LoD threshold would be removed. In this study, we present a new method of quantifying uncertainty associated with channel change based on probabilistic, spatially varying estimates of co‐registration error and digitization uncertainty that obviates a LoD threshold. The spatially distributed probabilistic (SDP) method can be applied to both linear channel adjustments and polygons of erosion and deposition, making this the first uncertainty method generalizable to all metrics of channel change. Using a case study from the Yampa River, Colorado, we show that the SDP method reduced the magnitude of uncertainty and enabled us to detect smaller channel changes as significant. Additionally, the distributional information provided by the SDP method allowed us to report the magnitude of channel change with an appropriate level of confidence in cases where a simple LoD approach yielded an indeterminate result.

Earth Surface Processes and Landforms↗

USGS2018

Hyperspectral remote sensing of wetland vegetation

Chapter 11 by Ramsey and Rangoonwala provides an overview of how hyperspectral imaging (HSI) advances the mapping of coastal wetlands that comprise a unique variety of plant species, forms, and associations. Each description begins by seeking to uncover the relationship between canopy hyperspectral reflectance and one or more of the aggregated biophysical properties of the wetland canopy: leaf spectral properties, canopy structure, or background reflectance. Examples incorporate the application of radiative transfer equations, direct measurements, and above the top of canopy reflectance and photography. First demonstrated is how HSI can elucidate relations observed with broadband mapping of mangroves as well as enhance change detection. Similar uses of HSI are suggested for the mapping of cypress and bottomland hardwood forests. Next, the mapping of invasive plants is used to demonstrate how HSI can be used to transfer high-spatial-resolution broadband information to spatial resolutions amenable to regional mapping. The demonstration also includes the fusion of HSI and broadband mapping for added-value regional risk assessment of invasive establishment. Illustrations and discussion then show that the proper interpretation of HSI canopy reflectance can require measurement of both the marsh canopy structure and its compositional biomass. The final HSI application demonstrates the capability to detect and track abnormal marsh change at the leaf and canopy levels within variable backgrounds. It further shows how necessary HSI spectral information can be identified and captured in a limited number of narrowbands for advancing operational mapping.

Book chapter↗

USGS2023

A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.

California↗

USGS2018

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Colorado Plateau↗

USGS2010

Use of wildlife webcams - Literature review and annotated bibliography

The U.S. Fish and Wildlife Service National Conservation Training Center requested a literature review product that would serve as a resource to natural resource professionals interested in using webcams to connect people with nature. The literature review focused on the effects on the public of viewing wildlife through webcams and on information regarding installation and use of webcams. We searched the peer reviewed, published literature for three topics: wildlife cameras, virtual tourism, and technological nature. Very few publications directly addressed the effect of viewing wildlife webcams. The review of information on installation and use of cameras yielded information about many aspects of the use of remote photography, but not much specifically regarding webcams. Aspects of wildlife camera use covered in the literature review include: camera options, image retrieval, system maintenance and monitoring, time to assemble, power source, light source, camera mount, frequency of image recording, consequences for animals, and equipment security. Webcam technology is relatively new and more publication regarding the use of the technology is needed. Future research should specifically study the effect that viewing wildlife through webcams has on the viewers' conservation attitudes, behaviors, and sense of connectedness to nature.

Open-File Report↗

USGS2016

Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)

Remote sensing has proven a useful way of evaluating long-term trends in vegetation “greenness” through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends. An assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.

Guangzhou↗

USGS2022

Climate disequilibrium dominates uncertainty in long-term projections of primary productivity

Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.

Ecology Letters↗

USGS2014

Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin

The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.

Alaska↗

USGS2006

Evaluation of eelgrass beds mapping using a high-resolution airborne multispectral scanner

Eelgrass ( Zostera marina ) can provide vital ecological functions in stabilizing sediments, influencing current dynamics, and contributing significant amounts of biomass to numerous food webs in coastal ecosystems. Mapping eelgrass beds is important for coastal water and nearshore estuarine monitoring, management, and planning. This study demonstrated the possible use of high spatial (approximately 5 m) and temporal (maximum low tide) resolution airborne multispectral scanner on mapping eelgrass beds in Northern Puget Sound, Washington. A combination of supervised and unsupervised classification approaches were performed on the multispectral scanner imagery. A normalized difference vegetation index (NDVI) derived from the red and near-infrared bands and ancillary spatial information, were used to extract and mask eelgrass beds and other submerged aquatic vegetation (SAV) in the study area. We evaluated the resulting thematic map (geocoded, classified image) against a conventional aerial photograph interpretation using 260 point locations randomly stratified over five defined classes from the thematic map. We achieved an overall accuracy of 92 percent with 0.92 Kappa Coefficient in the study area. This study demonstrates that the airborne multispectral scanner can be useful for mapping eelgrass beds in a local or regional scale, especially in regions for which optical remote sensing from space is constrained by climatic and tidal conditions.

Photogrammetric Engineering and Remote Sensing↗

USGS2017

Velocities and mass balance of Pine Island Glacier, West Antarctica, derived from ERS-1 SAR images

Pine Island Glacier is one of the major ice streams draining West Antarctica. We calculated average velocities for both its grounded and floating parts by tracking crevasses and other patterns moving with the ice on two sequential images acquired in February and December 1992 by ERS-1 SAR (European Remote-Sensing Satellite, Synthetic Aperture Radar). Velocities in the fast-moving central parts of the glacier range from about 1.3 km a −1 on the grounded part to about 2.6 km a −1 on the floating part. Velocity increases rapidly just below the grounding line. For the floating part, velocities are approximately 0.3 km a −1 faster than previously established from Landsat images by other workers. This observation suggests that the discharge is larger and, therefore, the mass balance less positive than previously thought; however, our study also shows that current information is insufficient to allow for precise mass-balance calculations. Images of the glacier front show that a calving event released an iceberg about 5 km wide between February and December 1992. The position of the glacier front has not changed substantially between 1973 (Landsat image) and 1992 (ERS-1 images).

Pine Island Glacier↗

USGS2015

Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice ( R 2 = 0.91); 925 and 1104 nm for alfalfa ( R 2 = 0.81); 722 and 732 nm for cotton ( R 2 = 0.97); and 529 and 895 nm for maize ( R 2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.

California↗

USGS2026

Demonstration, validation, and application of hyperspectral microscopy for the collection of cyanobacterial spectral signatures

Cyanobacterial and other algal blooms are an environmental concern in waterbodies worldwide. While these blooms are a nuisance for recreational activities, they can also be harmful to human and wildlife health when the algae produce and release toxins. Algal community composition can be monitored and analyzed by acquiring hyperspectral images that provide information on various photosynthetic and accessory pigments. Validated, traceable measurements are needed to compare data collected by different hyperspectral instruments. In this proof-of-concept study, we detail the development and validation of a custom hyperspectral microscopy imaging system and assess whether this technology can differentiate between cyanobacteria genera based on differences in their reflectance characteristics. As not all cyanobacteria produce toxins, the ability to distinguish among taxa could be used to identify potential toxin-producers and guide field sampling and further research. Spectral characterization of these taxa contributes to remote sensing efforts to characterize and identify cyanobacterial genera at larger spatial scales.

Oregon↗

USGS2020

The NASA hydrological forecast system for food and water security applications

Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning System Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multi-model, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage); and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multi-model ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its one-to-five month forecasts successfully capture known historic drought events. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

Bulletin of the American Meteorological Society↗

USGS1990

Geographic information system as country-level development and monitoring tool, Senegal example

Geographic information systems (GIS) allow an investigator the capability to merge and analyze numerous types of country-level resource data. Hypothetical resource analysis applications in Senegal were conducted to illustrate the utility of a GIS for development planning and resource monitoring. Map and attribute data for soils, vegetation, population, infrastructure, and administrative units were merged to form a database within a GIS. Several models were implemented using a GIS to: analyze development potential for sustainable dryland agriculture; prioritize where agricultural development should occur based upon a regional food budget; and monitor dynamic events with remote sensing. The steps for implementing a GIS analysis are described and illustrated, and the use of a GIS for conducting an economic analysis is outlined. Using a GIS for analysis and display of results opens new methods of communication between resource scientists and decision makers. Analyses yielding country-wide map output and detailed statistical data for each level of administration provide the advantage of a single system that can serve a variety of users.

Conference Paper↗

USGS2009

Selected achievements, science directions, and new opportunities for the WEBB small watershed research program

Over nearly two decades, the Water, Energy, and Biogeochemical Budgets (WEBB) small watershed research program of the U.S. Geological Survey (USGS) has documented how water and solute fluxes, nutrient, carbon, and mercury dynamics, and weathering and sediment transport respond to natural and humancaused drivers, including climate, climate change, and atmospheric deposition. Together with a continued and increasing focus on the effects of climate change, more investigations are needed that examine ecological effects (e.g., evapotranspiration, nutrient uptake) and responses (e.g., species abundances, biodiversity) that are coupled with the physical and chemical processes historically observed in the WEBB program. Greater use of remote sensing, geographic modeling, and habitat/watershed modeling tools is needed, as is closer integration with the USGS-led National Phenology Network. Better understanding of process and system response times is needed. The analysis and observation of land-use and climate change effects over time should be improved by pooling data obtained by the WEBB program during the last two decades with data obtained earlier and (or) concurrently from other research and monitoring studies conducted at or near the five WEBB watershed sites. These data can be supplemented with historical and paleo-environmental information, such as could be obtained from tree rings and lake cores. Because of the relatively pristine nature and small size of its watersheds, the WEBB program could provide process understanding and basic data to better characterize and quantify ecosystem services and to develop and apply indicators of ecosystem health. In collaboration with other Federal and State watershed research programs, the WEBB program has an opportunity to contribute to tracking the short-term dynamics and long-term evolution of ecosystem services and health indicators at a multiplicity of scales across the landscape.

Conference Paper↗