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Results for “Remote Sensing Information”

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At least 55 records · Page 3Linked to original sources
USGS2005

Planning for bird conservation: a tale of two models

Planning for bird conservation has become increasingly reliant on remote sensing, geographical information systems, and, especially, models used to predict the occurrence of bird species as well as their density and demographics. We address the role of such tools by contrasting two models used in bird conservation. One, the Mallard ( Anas platyrhynchos) productivity model, is very detailed, mechanistic, and based on an enormous body of research. The Mallard model has been extensively used with success to guide management efforts for Mallards and certain other species of ducks. The other model, the concept of Bird Conservation Areas, is more simple, less mechanistic, and less well-grounded in research. This concept proposes that large patches of suitable habitat in a proper landscape will be adequate to maintain populations of birds. The Bird Conservation Area concept recently has been evaluated in the northern tallgrass prairie, where its fundamental assumptions have been found not to hold consistently. We argue that a more comprehensive understanding of the biology of individual species, and how they respond to habitat features, will be essential before we can use remotely sensed information and geographic information system products with confidence.

General Technical Report↗
USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗
USGS2000

Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System

Pesticides used in agriculture may cause adverse health effects among the population living near agricultural areas. However, identifying the populations most likely to be exposed is difficult. We conducted a feasibility study to determine whether satellite imagery could be used to reconstruct historical crop patterns. We used historical Farm Service Agency records as a source of ground reference data to classify a late summer 1984 satellite image into crop species in a three-county area in south central Nebraska. Residences from a population-based epidemiologic study of non-Hodgkin lymphoma were located on the crop maps using a geographic information system (GIS). Corn, soybeans, sorghum, and alfalfa were the major crops grown in the study area. Eighty-five percent of residences could be located, and of these 22% had one of the four major crops within 500 m of the residence, an intermediate distance for the range of drift effects from pesticides applied in agriculture. We determined the proximity of residences to specific crop species and calculated crop-specific probabilities of pesticide use based on available data. This feasibility study demonstrated that remote sensing data and historical records on crop location can be used to create historical crop maps. The crop pesticides that were likely to have been applied can be estimated when information about crop-specific pesticide use is available. Using a GIS, zones of potential exposure to agricultural pesticides and proximity measures can be determined for residences in a study.

Nebraska↗
USGS1995

Recent data and information system initiatives for remotely sensed measurements of the land surface

As part of the International Satellite Land Satellite Climatology Program (ISLSCP) Workshop on Remote Sensing of the Land Surface for Studies of Global Change, five invited presentations were given on recent data initiatives relevant to the ISLSCP community. The presentations are summarized in this paper along with some observations by the authors on data systems for the land sciences community. The invited presentations are by no means all inclusive but were selected as examples of current data activities, representing a range of topics associated with data for land sciences including: the generation of global and local scale data sets, the reworking of historical data sets, new data initiatives and some programmatic aspects of land data base development. This paper serves to provide information on these data initiatives and to air some of the issues concerning land science data systems that were raised at the meeting.

Remote Sensing of Environment↗
USGS1978

Sources of remotely sensed data

NCIC was established within the USGS to provide a single-point contact source for cartographic-related information, including remotely sensed data. A computerized indexing system, the Aerial Photography Summary Record System (APSRS), shows all holding for Federal agencies, with the long range goal of including data acquired on the state and local levels and (eventually) by private industry. The system directs the used to a particular agency which holds coverage over a particular unit area, based on the 7 1/2 minute USGS quadrangle system. The data will remain in the hands of the source agency.

Pecora IV: Application of Remote Sensing Data to W↗
USGS1981

The Earth Resources Observation Systems data center's training technical assistance, and applications research activities

The Earth Resources Observation Systems (EROS) Data Center (EDO, administered by the U.S. Geological Survey, U.S. Department of the Interior, provides remotely sensed data to the user community and offers a variety of professional services to further the understanding and use of remote sensing technology. EDC reproduces and sells photographic and electronic copies of satellite images of areas throughout the world. Other products include aerial photographs collected by 16 organizations, including the U.S. Geological Survey and the National Aeronautics and Space Administration. Primary users of the remotely sensed data are Federal, State, and municipal government agencies, universities, foreign nations, and private industries. The professional services available at EDC are primarily directed at integrating satellite and aircraft remote sensing technology into the programs of the Department of the Interior and its cooperators. This is accomplished through formal training workshops, user assistance, cooperative demonstration projects, and access to equipment and capabilities in an advanced data analysis laboratory. In addition, other Federal agencies, State and local governments, universities, and the general public can get assistance from the EDC Staff. Since 1973, EDC has contributed to the accelerating growth in development and operational use of remotely sensed data for land resource problems through its role as educator and by conducting basic and applied remote sensing applications research. As remote sensing technology continues to evolve, EDC will continue to respond to the increasing demand for timely information on remote sensing applications. Questions most often asked about EDC's research and training programs include: Who may attend an EDC remote sensing training course? Specifically, what is taught? Who may cooperate with EDC on remote sensing projects? Are interpretation services provided on a service basis? This report attempts to define the goals and objectives of and policies on the following EDC services: Training Program. User Assistance. Data Analysis Laboratory. Cooperative Demonstration Projects. Research Projects.

Open-File Report↗
USGS2024

Joint Agency Commercial Imagery Evaluation (JACIE)

The Joint Agency Commercial Imagery Evaluation (JACIE) was formed to leverage resources from several Federal agencies for the characterization of remote sensing data and to share those results across the remote sensing community (U.S. Geological Survey, 2024). Remote sensing data and the quality of that data are vital to (1) understanding the physical world and (2) supporting the science and engineering applications that strive to advance that understanding. The growing number of remotely sensed data sources offers users more choices. Understanding the characteristics and capabilities of current and new data sources, along with the quality of data they provide, is an important function of the multi-agency JACIE team. By performing data-quality analysis of civil and commercial remote sensing data and information products, the JACIE team provides the remote sensing community with awareness and independent verification of image data quality.

Fact Sheet↗
USGS2020

A geospatially resolved wetland vulnerability index: Synthesis of physical drivers

Assessing wetland vulnerability to chronic and episodic physical drivers is fundamental for establishing restoration priorities. We synthesized multiple data sets from E.B Forsythe National Wildlife Refuge, New Jersey, to establish a wetland vulnerability metric that integrates a range of physical processes, regulatory information and physical/biophysical features. The geospatial data are based on aerial imagery, remote sensing, regulatory information, and hydrodynamic modeling, and include elevation, tidal range, unvegetated to vegetated marsh ratio (UVVR), shoreline erosion, potential exposure to contaminants, residence time, marsh condition change, change in salinity and salinity exposure, and sediment concentration. First, we delineated the wetland complex into individual marsh units based on surface contours and then defined a wetland vulnerability index that combined contributions from all parameters. We applied principal component and cluster analyses to explore the interrelations between the data layers and separate regions that exhibited common characteristics. Our analysis shows that the spatial variation of vulnerability in this domain cannot be explained satisfactorily by a smaller subset of the variables. The most influential factor on the vulnerability index was the combined effect of elevation, tide range, residence time, and UVVR. Tide range and residence time had the highest correlation, and similar bay-wide spatial variation. Some variables (e.g., shoreline erosion) had no significant correlation with the rest of the variables. The aggregated index based on the complete dataset allows us to assess the overall state of a given marsh unit and quickly locate the most vulnerable units in a larger marsh complex. The application of geospatially complete datasets and consideration of chronic and episodic physical drivers represents an advance over traditional point-based methods for wetland assessment.

New Jersey↗
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↗
USGS2018

The Central African Republic Diamond Database—A geodatabase of archival diamond occurrences and areas of recent artisanal and small-scale diamond mining

The alluvial diamond deposits of the Central African Republic (CAR) are mined almost exclusively by way of informal artisanal and small-scale mining (ASM) methods. ASM sites range in diameter from a few meters to 30 meters or more, and are typically excavated by crews of diggers using hand tools, sieves, and jigs. CAR’s reported annual production has ranged from 300,000 to 470,000 carats over the past decade. This production is significant for CAR because it accounts for a large portion of the country’s export income and employs an estimated 60,000 to 90,000 miners nationally. Diamond production has also been linked to the violent conflict and political instability which have plagued the country for decades. The most recent conflict began in 2012 and resulted in an international embargo on the export of rough diamonds from CAR. This embargo was followed by a ceasefire and a return of peace in certain zones of the country in 2015; however, political and economic instability continues to afflict many areas of the country. International efforts to restore peace in CAR have included United Nations support as well as international technical assistance in tracking, assessing, and monitoring diamond production. In 2015, the Kimberley Process (KP) developed an operational framework allowing for legitimate exports from five subprefectures in CAR that were deemed to be compliant with KP internal controls and which were also considered to be free from systematic violence or control of armed groups. The goal of this study was to address information gaps regarding the location and extent of diamond occurrences and mining activity through the integration of geologic research with remote sensing, geographic information systems analysis, and fieldwork. Effective and efficient monitoring of diamond mining activity using satellite imagery requires detailed understanding of the geographic distribution of diamond sources and mining activities. A two-phase methodology was developed to address the knowledge gaps. The first phase consisted of the creation of a comprehensive geospatial catalogue of diamond mining and occurrence locations from archival records such as historical maps, mining reports, academic publications, and field data. Building upon this locational database, the second phase consisted of the creation of a geospatial dataset cataloguing current mining activity locations through manual interpretation of recently acquired satellite imagery. The accuracy of this second geospatial dataset was then assessed using field observations made between 2016 and 2017 by the U.S. Agency for International Development’s Property Rights and Artisanal Diamond Development II project. This report presents a two-part geodatabase: part 1 contains the locations of diamond mine sites and occurrences from archival sources, and part 2 indicates areas of current or recent mining activity. This geodatabase is unique in its temporal and spatial extent and may be used to analyze the geographic distribution of CAR’s known diamond resources, to assess the effect of recent violent conflicts and KP actions on diamond produc-tion, to provide decision makers with information regarding small-scale diamond mining, and to improve the monitoring of mining in regions of the country prone to conflict.

Open-File Report↗
USGS2025

Assessing the potential for evaluation of wildland fire models using remotely sensed data—Summary proceedings from a U.S. Geological Survey workshop in 2024

On September 19, 2024, the U.S. Geological Survey (USGS) held a virtual workshop titled “Potential for Evaluation of Fire Models with Remote Sensing Data Workshop” to assess the feasibility of using remotely sensed datasets to evaluate next-generation wildland fire behavior models. Remote sensing and fire modelling experts gathered to: (1) assess the suitability of a variety of classified, commercial, and publicly available remotely sensed datasets for advancing fire model evaluation; (2) develop ideas on how to integrate remotely sensed data products with fire model inputs and outputs; and (3) identify any barriers and limitations to performing an evaluation of next-generation fire models. The USGS National Civil Applications Center, USGS Earth Resources Observation and Science Center, and USGS Fort Collins Ecosystem Science Center presented information on remote sensing datasets for three Arizona wildfire case studies. The development teams of the Fire Dynamics Simulator and QUIC-Fire fire behavior models presented their models and current evaluation methodologies. Interspersed with these presentations were discussions regarding how to expand current wildfire remote sensing data collection efforts beyond operational needs to assist in future fire modeling. Workshop participants agreed that several of the remote sensing datasets have potential for wildfire model evaluation. However, participants also identified several barriers and complications to performing a model evaluation including key gaps in wildfire datasets; uncertainties related to model fire-atmosphere reinitiation; lack of ground truthing and atmospheric correction of remotely sensed datasets; and differences in spatial, geolocation, radiometric, and temporal resolutions between the datasets and models. Further, the absence of standardized methodologies for image interpretation, poor understanding of sensor capabilities and limitations, and a lack of automation also hinder model evaluation efforts. Based on feedback from this workshop, USGS fire modelers are considering a project to address the uncertainties related to fire model reinitiation and encouraging fire practitioners to collaborate with remote sensing experts on wildland fires to improve data collection for a broader community of practice. Additionally, multiagency efforts are in development for a comprehensive cross-sensor validation and ground-truth campaign to test spatial, spectral, and geolocation sensor capabilities, determine limitations, and identify observational gaps for future sensor development and acquisition.

Arizona↗
USGS1982

Ground-water applications of remote sensing

Remote sensing can be used as a tool to inventory springs and seeps and to interpret lithology, structure, and ground-water occurrence and quality. Thermograms are the best images for inventory of seeps and springs. The steps in aquifer mapping are image analysis and interpretation and ground-water interpretation. A ground-water interpretation is derived from a conceptual geologic model by inferring aquifer characteristics and water salinity. The image selection process is very important for obtaining maximum geologic and hydrologic information from remotely sensed data. Remote sensing can contribute an image base map or geologic and hydrologic parameters, derived from the image, to the multiple data sets in a hydrologic information system. Various merging and integration techniques may then be used to obtain information from these data sets.

Open-File Report↗
USGS1997

Geospatial Technology Strategic Plan 1997-2000

Executive Summary -- Geospatial technology applications have been identified in many U.S. Geological Survey Biological Resources Division (BRD) proposals for grants awarded through internal and partnership programs. Because geospatial data and tools have become more sophisticated, accessible, and easy to use, BRD scientists frequently are using these tools and capabilities to enhance a broad spectrum of research activities. Bruce Babbitt, Secretary of the Interior, has acknowledged--and lauded--the important role of geospatial technology in natural resources management. In his keynote address to more than 5,500 people representing 87 countries at the Environmental Systems Research Institute Annual Conference (May 21, 1996), Secretary Babbitt stated, '. . .GIS [geographic information systems], if properly used, can provide a lot more than sets of data. Used effectively, it can help stakeholders to bring consensus out of conflict. And it can, by providing information, empower the participants to find new solutions to their problems.' This Geospatial Technology Strategic Plan addresses the use and application of geographic information systems, remote sensing, satellite positioning systems, image processing, and telemetry; describes methods of meeting national plans relating to geospatial data development, management, and serving; and provides guidance for sharing expertise and information. Goals are identified along with guidelines that focus on data sharing, training, and technology transfer. To measure success, critical performance indicators are included. The ability of the BRD to use and apply geospatial technology across all disciplines will greatly depend upon its success in transferring the technology to field biologists and researchers. The Geospatial Technology Strategic Planning Development Team coordinated and produced this document in the spirit of this premise. Individual Center and Program managers have the responsibility to implement the Strategic Plan by working within the policy and guidelines stated herein.

Information and Technology Report↗
USGS2017

Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting

The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.

Remote Sensing Applications: Society and Environme↗
USGS2015

Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR

The American Society of Photogrammetry and Remote Sensing defined remote sensing as the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell et al., 1983). Environmental Systems Research Institute (ESRI) in its geographic information system (GIS) dictionary defines remote sensing as “collecting and interpreting information about the environment and the surface of the earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the earth’s surface or from the atmosphere, or by sending signals transmitted from a device and reflected back to it (ESRI, 2014).” The usual source of passive remote sensing data is the measurement of reflected or transmitted electromagnetic radiation (EMR) from the sun across the electromagnetic spectrum (EMS); this can also include acoustic or sound energy, gravity, or the magnetic field from or of the objects under consideration. In this context, the simple act of reading this text is considered remote sensing. In this case, the eye acts as a sensor and senses the light reflected from the object to obtain information about the object. It is the same technology used by a handheld camera to take a photograph of a person or a distant scenic view. Active remote sensing, however, involves sending a pulse of energy and then measuring the returned energy through a sensor (e.g., Radio Detection and Ranging [RADAR], Light Detection and Ranging [LiDAR]). Thermal sensors measure emitted energy by different objects. Thus, in general, passive remote sensing involves the measurement of solar energy reflected from the Earth’s surface, while active remote sensing involves synthetic (man-made) energy pulsed at the environment and the return signals are measured and recorded.

Book chapter↗
USGS2023

Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: Informing Tribal restoration priorities

Riparian systems across the Southwest United States are extremely valuable for the human and ecological communities that engage with them. However, they have experienced substantial changes and stresses over the past century, including non-native vegetation expansion, vegetation die-offs, and increased fire activity. Vegetation management approaches, such as ecological restoration, may address some of these stressors as well as reduce the risk of future impacts. We apply remote sensing to inform restoration priorities along the upper Gila River within the San Carlos Apache Reservation and Upper Gila River watershed. First, we develop a spatially and temporally explicit trend analysis across three observed climate periods (1985–1993, 1993–2014, 2014–2021) using the Landsat-derived indices to quantify changes in riparian vegetation conditions. These maps can be used to identify areas potentially more at risk for degradation. Second, we analyze changes in riparian vegetation within a climate framework to better understand trends and the potential effect of climate change. Vegetation greenness has largely increased throughout the watershed despite intensifying drought conditions across our study period, though areas within the lower watershed have shown increased stress and higher rates of wildfire and other disturbances over the past 5-years. Nevertheless, small-scale restoration activities appear to show improving vegetation conditions, suggesting efficacy of these restoration activities. Results from this study may be integrated with restoration objectives to develop a restoration plan that will help riparian vegetation communities adapt to change.

Arizona↗