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At least 55 records · Page 3Linked to original sources
USGS2007

Consequences of land-cover misclassification in models of impervious surface

Model estimates of impervious area as a function of landcover area may be biased and imprecise because of errors in the land-cover classification. This investigation of the effects of land-cover misclassification on impervious surface models that use National Land Cover Data (NLCD) evaluates the consequences of adjusting land-cover within a watershed to reflect uncertainty assessment information. Model validation results indicate that using error-matrix information to adjust land-cover values used in impervious surface models does not substantially improve impervious surface predictions. Validation results indicate that the resolution of the landcover data (Level I and Level II) is more important in predicting impervious surface accurately than whether the land-cover data have been adjusted using information in the error matrix. Level I NLCD, adjusted for land-cover misclassification, is preferable to the other land-cover options for use in models of impervious surface. This result is tied to the lower classification error rates for the Level I NLCD. ?? 2007 American Society for Photogrammetry and Remote Sensing.

Photogrammetric Engineering and Remote Sensing↗
USGS2007

Geometric correction and digital elevation extraction using multiple MTI datasets

Digital Elevation Models (DEMs) are traditionally acquired from a stereo pair of aerial photographs sequentially captured by an airborne metric camera. Standard DEM extraction techniques can be naturally extended to satellite imagery, but the particular characteristics of satellite imaging can cause difficulties. The spacecraft ephemeris with respect to the ground site during image collects is the most important factor in the elevation extraction process. When the angle of separation between the stereo images is small, the extraction process typically produces measurements with low accuracy, while a large angle of separation can cause an excessive number of erroneous points in the DEM from occlusion of ground areas. The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of some sensors, such as the Multispectral Thermal Imager (MTI), allows for multiple collects of the same area from different perspectives. This functionality of MTI makes it a good candidate for the implementation of a DEM extraction algorithm using multiple images for improved accuracy. Evaluation of this capability and development of algorithms to geometrically model the MTI sensor and extract DEMs from multi-look MTI imagery are described in this paper. An RMS elevation error of 6.3-meters is achieved using 11 ground test points, while the MTI band has a 5-meter ground sample distance.

Photogrammetric Engineering and Remote Sensing↗
USGS2007

InSAR imaging of volcanic deformation over cloud-prone areas - Aleutian islands

Interferometric synthetic aperture radar (INSAR) is capable of measuring ground-surface deformation with centimeter-tosubcentimeter precision and spatial resolution of tens-of meters over a relatively large region. With its global coverage and all-weather imaging capability, INSAR is an important technique for measuring ground-surface deformation of volcanoes over cloud-prone and rainy regions such as the Aleutian Islands, where only less than 5 percent of optical imagery is usable due to inclement weather conditions. The spatial distribution of surface deformation data, derived from INSAR images, enables the construction of detailed mechanical models to enhance the study of magmatic processes. This paper reviews the basics of INSAR for volcanic deformation mapping and the INSAR studies of ten Aleutian volcanoes associated with both eruptive and noneruptive activity. These studies demonstrate that all-weather INSAR imaging can improve our understanding of how the Aleutian volcanoes work and enhance our capability to predict future eruptions and associated hazards.

Photogrammetric Engineering and Remote Sensing↗
USGS2007

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor’s field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure.

Photogrammetric Engineering and Remote Sensing↗
USGS2008

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Photogrammetric Engineering and Remote Sensing↗
USGS2008

Quantifying multi-temporal urban development characteristics in Las Vegas from Landsat and ASTER data

Urban development has expanded rapidly in Las Vegas, Nevada of the United States, over the last fifty years. A major environmental change associated with this urbanization trend is the transformation of the landscape from natural cover types to increasingly anthropogenic impervious surface. This research utilizes remote sensing data from both the Landsat and Terra-Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instruments in conjunction with digital orthophotography to estimate urban extent and its temporal changes by determining sub-pixel impervious surfaces. Percent impervious surface area has shown encouraging agreement with urban land extent and development density. Results indicate that total urban land-use increases approximately 110 percent from 1984 to 2002. Most of the increases are associated with medium-to high-density urban development. Places having significant increases in impervious surfaces are in the northwestern and southeastern parts of Las Vegas. Most high-density urban development, however, appears in central Las Vegas. Impervious surface conditions for 2002 measured from Landsat and ASTER satellite data are compared in terms of their accuracy.

Photogrammetric Engineering and Remote Sensing↗
USGS2009

Spectral distance decay

Remotely sensed data represents key information for character-izing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (OLS) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both OLS and quantile regression. Nonetheless, OLS regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression.

Photogrammetric Engineering and Remote Sensing↗
USGS2009

Large area scene selection interface (LASSI): Methodology of selecting landsat imagery for The Global Land Survey 2005

The Global Land Survey (GLS) 2005 is a cloud-free, orthorec-tified collection of Landsat imagery acquired during the 2004 to 2007 epoch intended to support global land-cover and ecological monitoring. Due to the numerous complexities in selecting imagery for the GLS2005, NASA and the U.S. Geological Survey (USGS) sponsored the development of an automated scene selection tool, the Large Area Scene Selection Interface (LASSI), to aid in the selection of imagery for this data set. This innovative approach to scene selection applied a user-defined weighting system to various scene parameters: image cloud cover, image vegetation greenness, choice of sensor, and the ability of the Landsat-7 Scan Line Corrector (SLC)-off pair to completely fill image gaps, among others. The parameters considered in scene selection were weighted according to their relative importance to the data set, along with the algorithm’s sensitivity to that weight. This paper describes the methodology and analysis that established the parameter weighting strategy, as well as the post-screening processes used in selecting the optimal data set for GLS2005.

Photogrammetric Engineering and Remote Sensing↗
USGS2009

Analysis of dynamic thresholds for the normalized difference water index

The normalized difference water index (NDWI) has been successfully used to delineate surface water features. However, two major problems have been often encountered: (a) NDWIs calculated from different band combinations [visible, nearinfrared, or shortwave-infrared (SWIR)] can generate different results, and (b) NDWI thresholds vary depending on the proportions of subpixel water/non-water components. We need to evaluate all the NDWIS for determining the best performing index and to establish appropriate thresholds for clearly identifying water features. We used the spectral data obtained from a spectral library to simulate the satellite sensors Landsat ETM+, SPOT-5, ASTER, and MODIS, and calculated the simulated NDWI in different forms. We found that the NDWI calculated from (green - swm)/(green + SWIR), where SWIR is the shorter wavelength region (1.2 to 1.8 ??m), has the most stable threshold. We recommend this NDWI be employed for mapping water, but adjustment of the threshold based on actual situations is necessary. ?? 2009 American Society for Photogrammetry and Remote Sensing.

Photogrammetric Engineering and Remote Sensing↗
USGS2009

Influence of resolution in irrigated area mapping and area estimation

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R 2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that “the finer the spatial resolution of the sensor used, greater was the irrigated area derived,” since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and non-irrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent).

Krishna River basin↗
USGS2009

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Photogrammetric Engineering and Remote Sensing↗
USGS2009

Developing collaborative classifiers using an expert-based model

This paper presents a hierarchical, multi-stage adaptive strategy for image classification. We iteratively apply various classification methods (e.g., decision trees, neural networks), identify regions of parametric and geographic space where accuracy is low, and in these regions, test and apply alternate methods repeating the process until the entire image is classified. Currently, classifiers are evaluated through human input using an expert-based system; therefore, this paper acts as the proof of concept for collaborative classifiers. Because we decompose the problem into smaller, more manageable sub-tasks, our classification exhibits increased flexibility compared to existing methods since classification methods are tailored to the idiosyncrasies of specific regions. A major benefit of our approach is its scalability and collaborative support since selected low-accuracy classifiers can be easily replaced with others without affecting classification accuracy in high accuracy areas. At each stage, we develop spatially explicit accuracy metrics that provide straightforward assessment of results by non-experts and point to areas that need algorithmic improvement or ancillary data. Our approach is demonstrated in the task of detecting impervious surface areas, an important indicator for human-induced alterations to the environment, using a 2001 Landsat scene from Las Vegas, Nevada.

Nevada↗
USGS2011

Daily MODIS data trends of hurricane-induced forest impact and early recovery

We studied the use of daily satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors to assess wetland forest damage and recovery from Hurricane Katrina (29 August 2005 landfall). Processed MODIS daily vegetation index (VI) trends were consistent with previously determined impact and recovery patterns provided by the "snapshot" 25 m Landsat Thematic Mapper optical and RADARSAT-1 synthetic aperture radar satellite data. Phenological trends showed high 2004 and 2005 pre-hurricane temporal correspondence within bottomland hardwood forest communities, except during spring green-up, and temporal dissimilarity between these hardwoods and nearby cypress-tupelo swamp forests ( Taxodium distichum [baldcypress] and Nyssa aquatica [water tupelo]). MODIS VI trend analyses established that one year after impact, cypress-tupelo and lightly impacted hardwood forests had recovered to near pre-hurricane conditions. In contrast, canopy recovery lagged in the moderately and severely damaged hardwood forests, possibly reflecting regeneration of pre-hurricane species and stand-level replacement by invasive trees.

Louisiana↗
USGS2011

USGS remote sensing coordination for the 2010 Haiti earthquake

In response to the devastating 12 January 2010, earthquake in Haiti, the US Geological Survey (USGS) provided essential coordinating services for remote sensing activities. Communication was rapidly established between the widely distributed response teams and data providers to define imaging requirements and sensor tasking opportunities. Data acquired from a variety of sources were received and archived by the USGS, and these products were subsequently distributed using the Hazards Data Distribution System (HDDS) and other mechanisms. Within six weeks after the earthquake, over 600,000 files representing 54 terabytes of data were provided to the response community. The USGS directly supported a wide variety of groups in their use of these data to characterize post-earthquake conditions and to make comparisons with pre-event imagery. The rapid and continuing response achieved was enabled by existing imaging and ground systems, and skilled personnel adept in all aspects of satellite data acquisition, processing, distribution and analysis. The information derived from image interpretation assisted senior planners and on-site teams to direct assistance where it was most needed.

Photogrammetric Engineering and Remote Sensing↗
USGS2011

Seismic zonation of Port-Au-Prince using pixel- and object-based imaging analysis methods on ASTER GDEM

We report about a preliminary study to evaluate the use of semi-automated imaging analysis of remotely-sensed DEM and field geophysical measurements to develop a seismic-zonation map of Port-au-Prince, Haiti. For in situ data, V S 30 values are derived from the MASW technique deployed in and around the city. For satellite imagery, we use an ASTER GDEM of Hispaniola. We apply both pixel- and object-based imaging methods on the ASTER GDEM to explore local topography (absolute elevation values) and classify terrain types such as mountains, alluvial fans and basins/near-shore regions. We assign NEHRP seismic site class ranges based on available V S 30 values. A comparison of results from imagery-based methods to results from traditional geologic-based approaches reveals good overall correspondence. We conclude that image analysis of RS data provides reliable first-order site characterization results in the absence of local data and can be useful to refine detailed site maps with sparse local data.

Port-au-Prince↗
USGS2012

Generation of a U.S. national urban land use product

Characterization of urban land uses is essential for many applications. However, differentiating among thematically-detailed urban land uses (residential, commercial, industrial, institutional, recreational, etc.) over broad areas is challenging, in part because image-based solutions are not ideal for establishing the contextual basis for identifying economic function and use. At present no current United States national-scale mapping exists for urban land uses similar to the classical Anderson Level II classification. This paper describes a product that maps urban land uses, and is linked to and corresponds with the National Land Cover Database (NLCD) 2006. In this product, NLCD urban pixels, in addition to their current imperviousness intensity classification, are assigned one of nine urban use classes based on information drawn from multiple data sources. These sources include detailed infrastructure information, population characteristics, and historical land use. The result is a method for creating a 30 m national-scale grid providing thematically-detailed urban land use information which complements the NLCD. Initial results for 10 major metropolitan areas are provided as an on-line link. Accuracy assessment of initial products yielded an overall accuracy of 81.6 percent.

Photogrammetric Engineering and Remote Sensing↗
USGS2014

Integrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions

With the acquisition of lidar data for over 30 percent of the US, it is now possible to assess the three-dimensional distribution of features at the national scale. This paper integrates over 350 billion lidar points from 28 disparate datasets into a national-scale database and evaluates if height above ground is an important variable in the context of other nationalscale layers, such as the US Geological Survey National Land Cover Database and the US Environmental Protection Agency ecoregions maps. While the results were not homoscedastic and the available data did not allow for a complete height census in any of the classes, it does appear that where lidar data were used, there were detectable differences in heights among many of these national classification schemes. This study supports the hypothesis that there were real, detectable differences in heights in certain national-scale classification schemes, despite height not being a variable used in any of the classification routines.

Photogrammetric Engineering and Remote Sensing↗
USGS2014

Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing

Increasing pressure to feed the growing population with scarce water resources requires accurate and routine cropland mapping. This paper develops and implements a rule-based automated cropland classification algorithm (ACCA) using multi-sensor remote sensing data. Pixel-by-pixel accuracy assessments showed that ACCA produced an overall accuracy of 96 percent (K hat = 0.8) when tested using independent data layers. Furthermore, ACCA-generated county cropland areas showed high agreement (R-square values 0.94) when compared with three independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data for the 58 counties in California. Our results demonstrate the ability of ACCA to generate cropland extent and areas over space and time, in an automated fashion with high degree of accuracies year after year, greatly contributing to food and water security analysis and decision making.

California↗