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At least 1,315 records · Page 73Linked to original sources
USGS2009

Providing public standardized data access function: Lessons learned from accessing USGS Landsat archive

The geospatial community is experiencing a shift from having data locally to sharing them over the Web. However, not all the data accessing systems are built in compliance with open geospatial standards and thus are weak in terms of interoperability. The USGS Landsat data are now available through free electronic access though not yet through standard Web service interfaces. This paper intends to discuss the experience and lessons learned from integrating a public data access function to the USGS Landsat data archive into a geospatial workflow environment. Currently available systems and their problems, proposed solutions and application scenarios are discussed.

Conference Paper↗
USGS2009

Web-based decision support and visualization tools for water quality management in the Chesapeake Bay watershed

Federal, State, and local water quality managers charged with restoring the Chesapeake Bay ecosystem require tools to maximize the impact of their limited resources. To address this need, the U.S. Geological Survey (USGS) and the Environmental Protection Agency's Chesapeake Bay Program (CBP) are developing a suite of Web-based tools called the Chesapeake Online Assessment Support Toolkit (COAST). The goal of COAST is to help CBP partners identify geographic areas where restoration activities would have the greatest effect, select the appropriate management strategies, and improve coordination and prioritization among partners. As part of the COAST suite of tools focused on environmental restoration, a water quality management visualization component called the Nutrient Yields Mapper (NYM) tool is being developed by USGS. The NYM tool is a web application that uses watershed yield estimates from USGS SPAtially Referenced Regressions On Watershed (SPARROW) attributes model (Schwarz et al., 2006) [6] to allow water quality managers to identify important sources of nitrogen and phosphorous within the Chesapeake Bay watershed. The NYM tool utilizes new open source technologies that have become popular in geospatial web development, including components such as OpenLayers and GeoServer. This paper presents examples of water quality data analysis based on nutrient type, source, yield, and area of interest using the NYM tool for the Chesapeake Bay watershed. In addition, we describe examples of map-based techniques for identifying high and low nutrient yield areas; web map engines; and data visualization and data management techniques.

Conference Paper↗
USGS2010

A method for land surveying sampling optimization strategy

At present, how to select a limited but representative sample dataset from the existing land information database to guide the new round of land survey and assessment sampling is a critical issue for land sampling strategy study. As a case study to determine and analyze the sample capacity and sample spatial location of land survey sampling for the study area, Panyu District in Guangzhou, the paper developed the strategy based on the combination of classical sampling technique and geographical model under a certain confidence level and estimation accuracy requirement, and the performance of the sampling strategy was then evaluated by the Global Geary's C and the Quick-BP neural network model respectively. The test result showed that, compared with traditional c-means clustering sampling method, the accuracy of the sampling prediction based on local Moran index spatial clustering sampling method was increased by 13.57% which abstracted better the land information in the database.

Guangzhou↗
USGS2009

Web-client based distributed generalization and geoprocessing

Generalization and geoprocessing operations on geospatial information were once the domain of complex software running on high-performance workstations. Currently, these computationally intensive processes are the domain of desktop applications. Recent efforts have been made to move geoprocessing operations server-side in a distributed, web accessible environment. This paper initiates research into portable client-side generalization and geoprocessing operations as part of a larger effort in user-centered design for the US Geological Survey's The National Map. An implementation of the Ramer-Douglas-Peucker (RDP) line simplification algorithm was created in the open source OpenLayers geoweb client. This algorithm implementation was benchmarked using differing data structures and browser platforms. The implementation and results of the benchmarks are discussed in the general context of client-side geoprocessing. (Abstract).

Conference Paper↗
USGS2011

Earth observing data and methods for advancing water harvesting technologies in the semi-arid rain-fed environments of India

The paper develops approaches and methods of modeling and mapping land and water productivity of rain-fed crops in semi-arid environments of India using hyperspectral, hyperspatial, and advanced multispectral remote sensing data and linking the same to field-plot data and climate station data. The overarching goal is to provide information to advance water harvesting technologies in the agricultural croplands of the semi-arid environments of India by conducting research in a representative pilot site in Jodhpur, Rajasthan, India. ?? 2011 IEEE.

Conference Paper↗
USGS2017

Multi-level multi-task learning for modeling cross-scale interactions in nested geospatial data

Predictive modeling of nested geospatial data is a challenging problem as the models must take into account potential interactions among variables defined at different spatial scales. These cross-scale interactions, as they are commonly known, are particularly important to understand relationships among ecological properties at macroscales. In this paper, we present a novel, multi-level multi-task learning framework for modeling nested geospatial data in the lake ecology domain. Specifically, we consider region-specific models to predict lake water quality from multi-scaled factors. Our framework enables distinct models to be developed for each region using both its local and regional information. The framework also allows information to be shared among the region-specific models through their common set of latent factors. Such information sharing helps to create more robust models especially for regions with limited or no training data. In addition, the framework can automatically determine cross-scale interactions between the regional variables and the local variables that are nested within them. Our experimental results show that the proposed framework outperforms all the baseline methods in at least 64% of the regions for 3 out of 4 lake water quality datasets evaluated in this study. Furthermore, the latent factors can be clustered to obtain a new set of regions that is more aligned with the response variables than the original regions that were defined a priori from the ecology domain.

Conference Paper↗
USGS2021

Partial differential equation driven dynamic graph networks for predicting stream water temperature

This paper presents a physics-guided machine learning approach that incorporates partial differential equations (PDEs) in a graph neural network model to improve the prediction of water temperature in river networks. The standard graph neural network model often uses pre-defined edge weights based on distance or similarity measures. Such static graph structure can be limited in capturing multiple processes in a physical system that interact and evolve over time. The limitation to represent underlying physical processes can severely affect the performance of the predictive model, especially when we have access to limited training data. To better capture the dynamic interactions among multiple segments in a river network, we built a dynamic graph model, where the graph structure is driven by the PDE that describes underlying physical processes. We further combine the dynamic graph structure and the recurrent layers to model temporal dependencies and improve the prediction. We demonstrate the effectiveness of the proposed method in a subnetwork of the Delaware River Basin. In particular, we show that the proposed method outperforms existing physics-based and machine learning models in temperature prediction using sparse observation data for training. The proposed method has also been shown to produce better performance when generalized to different seasons.

Conference Paper↗
USGS2011

Study on a pattern classification method of soil quality based on simplified learning sample dataset

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

Conference Paper↗
USGS2021

SUAS and machine learning integration in waterfowl population surveys

The rapid technological development of small Unmanned Aircraft Systems (sUAS) has led to an increase in capabilities of aerial image collection and analysis for monitoring a variety of wildlife species including waterfowl. Biologists mainly rely on conducting ocular surveys from fixed-wing aircraft or helicopters to estimate waterfowl abundance. sUAS provide an alternative that is safer, less expensive, and more flexible. Researchers have attempted to estimate waterfowl abundance from aerial imagery, but this method has proven to be too time consuming. Machine learning provides the opportunity to more efficiently estimate waterfowl abundance from aerial imagery. In this paper, we present a new integrated system of sUAS and machine learning for waterfowl population surveys. This system provides a user-friendly process for sUAS survey design, deployment, and data post-processing using deep learning methods to automatically detect and count waterfowl. To develop this system, we conducted many sUAS flights to capture a diversity of imagery and assembled six datasets of imagery taken from both fix-winged aircraft and sUAS flights. We used these datasets to develop and evaluate state-of-the-art deep learning models for waterfowl detection. Our system of using a combination of sUAS and machine learning has proved to be an efficient and accurate approach for collecting, analyzing, and estimating waterfowl abundance.

Conference Paper↗
USGS1995

Microwave radiometers and their application in field and aircraft campaigns for remote sensing of land and water surfaces

The paper presents an overview of the scientific investigations and practical applications of a group of specialists working in the Institute of Radioengineering and Electronics of the Russian Academy of Sciences (IRE RAS) with their partners in Russia and the USA in the area of passive microwave remote sensing as applied to the study of soil, vegetation and water bodies. Applications that are described include problems related to agriculture, land reclamation, water management, wetlands, and highways.

Conference Paper↗
USGS1996

Ecosystem management: A decision support GIS approach

This paper describes a new approach using decision support GIS for handling information in ecosystem management. Technical research efforts resulted in specialized spatial decision support systems for wetland restoration planning, wetland permit analysis, and wildlife research and management. These applications are briefly presented to illustrate the usage of the methodology. With its powerful capabilities in information synthesis, analytical visualization, and spatial simulation and modeling, the decision support GIS provided cost-effective technical tools and a conceptual framework for integrating natural resource management with an ecosystem approach.

Conference Paper↗
USGS2002

Interferometric synthetic aperture radar studies of Alaska volcanoes

Interferometric synthetic aperture radar (InSAR) imaging is a recently developed geodetic technique capable of measuring ground-surface deformation with centimeter to subcentimeter vertical precision and spatial resolution of tens-of-meter over a relatively large region (/spl sim/10/sup 4/ km/sup 2/). The spatial distribution of surface deformation data, derived from InSAR images, enables the construction of detailed mechanical models to enhance the study of magmatic and tectonic processes associated with volcanoes. This paper summarizes our recent InSAR studies of several Alaska volcanoes, which include Okmok, Akutan, Kiska, Augustine, Westdahl, and Peulik volcanoes.

Alaska↗
USGS2004

The effects of wavelet compression on Digital Elevation Models (DEMs)

This paper investigates the effects of lossy compression on floating-point digital elevation models using the discrete wavelet transform. The compression of elevation data poses a different set of problems and concerns than does the compression of images. Most notably, the usefulness of DEMs depends largely in the quality of their derivatives, such as slope and aspect. Three areas extracted from the U.S. Geological Survey's National Elevation Dataset were transformed to the wavelet domain using the third order filters of the Daubechies family (DAUB6), and were made sparse by setting 95 percent of the smallest wavelet coefficients to zero. The resulting raster is compressible to a corresponding degree. The effects of the nulled coefficients on the reconstructed DEM are noted as residuals in elevation, derived slope and aspect, and delineation of drainage basins and streamlines. A simple masking technique also is presented, that maintains the integrity and flatness of water bodies in the reconstructed DEM.

Conference Paper↗
USGS2004

Analysis of summer 2002 melt extent on the Greenland ice sheet using MODIS and SSM/I data

Previous work has shown that the summer of 2002 had the greatest area of snow melt extent on the Greenland ice sheet ever recorded using passive-microwave data. In this paper, we compare the 0deg isotherm derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument, with Special Sensor Microwave/Imager (SSM/I)-derived melt, at the time of the maximum melt extent in 2002. To validate the MODIS-derived land-surface temperatures (LSTs), we compared the MODIS LSTs with air temperatures from nine stations (using 11 different data points) and found that they agreed to within 2.3 plusmn 2.09 degC, with station temperatures consistently lower than the MODIS LSTs. According to the MODIS LST, the maximum surface melt extended to ~2300 m in southern Greenland; while the SSM/I measurements showed that the maximum melt extended to nearly 2700 m in southeastern Greenland. The MODIS and SSM/I data are complementary in providing detailed information about the progression of surface and near-surface melt on the Greenland ice sheet.

Conference Paper↗
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↗
USGS2008

Detection of foliage-obscured vehicle using a multiwavelength polarimetric lidar

Foliage obscured man-made targets detection and identification is of great interest to many applications. In this paper, the backscattered laser signals from a multiwavelength polarimetric lidar were used to detect a vehicle hidden inside a vegetated area. The polarimetric reflectance data from the lidar at two separate laser wavelengths at 1064 nm and 532 nm revealed distinct target characteristics from both the vehicle and the vegetation. The results from this case study demonstrated the validity of the proposed lidar detection technique. Furthermore, the results could potentially lead to a lidar detection and identification technique for a wide variety of foliage-obscured man-made targets under various application scenarios.

Conference Paper↗
USGS2008

Preliminary radiometric calibration assessment of ALOS AVNIR-2

This paper summarizes the activities carried out in the frame of the data quality activities of the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensor onboard the Advanced Land Observing Satellite (ALOS). Assessment of the radiometric calibration of the AVNIR-2 multi-spectral imager is achieved via three intercomparisons to currently flying sensors over the Libyan desert, during the first year of operation. All three methodologies indicate a slight underestimation of AVNIR-2 in band 1 by 4 to 7 % with respect to other sensors radiometric scale. Band 2 does not show any obvious bias. Results for band 3 are affected by saturation due to inappropriate gain setting. Two methodologies indicate no significant bias in band 4. Preliminary results indicate possible degradations of the AVNIR-2 channels, which, when modeled as an exponentially decreasing functions, have time constants of respectively 13.2 %.year -1 , 8.8 %.year -1 and 0.1 %.year -1 in band 1, 2 and 4 (with respect to the radiometric scale of the MEdium Resolution Imaging Spectrometer, MERIS). Longer time series of AVNIR-2 data are needed to draw final conclusions.

Conference Paper↗
USGS2007

Radiometric recalibration procedure for Landsat-5 Thematic Mapper data

The Landsat-5 (L5) satellite was launched on March 01, 1984, with a design life of three years. Incredibly, the L5 Thematic Mapper (TM) has collected data for 23 years. Over this time, the detectors have aged, and its radiometric characteristics have changed since launch. The calibration procedures and parameters have also changed with time. Revised radiometric calibrations have improved the radiometric accuracy of recently processed data; however, users with data that were processed prior to the calibration update do not benefit from the revisions. A procedure has been developed to give users the ability to recalibrate their existing Level 1 (Ll) products without having to purchase reprocessed data from the U.S. Geological Survey (USGS). The accuracy of the recalibration is dependent on the knowledge of the prior calibration applied to the data. The “Work Order” File, included with standard National Land Archive Production System (NLAPS) data products, gives parameters that define the applied calibration. These are the Internal Calibrator (IC) calibration parameters or the default prelaunch calibration, if there were problems with the IC calibration. This paper details the recalibration procedure for data processed using IC, in which users have the Work Order file.

Conference Paper↗