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At least 55 records · Page 3Linked to original sources
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↗
USGS2014

Examining change detection approaches for tropical mangrove monitoring

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Photogrammetric Engineering and Remote Sensing↗
USGS2014

Comparison of simulated HyspIRI with two multispectral sensors for invasive species mapping

This paper assesses the potential of a single HYSPIRI scene to estimate cover of the non-native invasive buffelgrass (Pennisetum ciliare) in a heterogeneous Sonoran Desert scrub ecosystem. We simulated HYSPIRI (60 m) along with two multispectral sensors, Thematic Mapper (TM; 30 m) and Advanced Space-borne Thermal Emission and Reflection Spectrometer (ASTER; 15 m), from high-resolution Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 3.2 m) imagery in an area infested by buffelgrass near Tucson, Arizona. We compared classification accuracies of all simulated sensors at spatial resolutions of 15 m, 30 m, and 60 m to evaluate tradeoffs of spectral and spatial resolution across the sensors. Although spectroscopically superior to Landsat TM and ASTER, ASTER easily outperformed HYSPIRI for small infestations (225 m 2 ) on account of its spatial resolution. Shortwave-infrared bands near 2.2 µm were key indicators for both HYSPIRI and ASTER, highlighting the benefit of narrow-wave SWIR for mapping invasive species in arid ecosystems.

Photogrammetric Engineering and Remote Sensing↗
USGS2014

Annual crop type classification of the U.S. Great Plains for 2000 to 2011

The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophysical environmental characteristics. This study accounted for the most prevalent crop types in the region, including, corn, soybeans, winter wheat, spring wheat, cotton, sorghum, and alfalfa. Annual CTM crop maps of the US Great Plains were created for 2000 to 2011 at a spatial resolution of 250 meters. The CTM achieved an 87 percent classification success rate on 1.8 million observation points that were withheld from model training. Product validation was performed on greater than 15,000 county records with a coefficient of determination of R 2 = 0.76.

Great Plains↗
USGS2014

Biomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission

New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniques: two-band hyperspectral vegetation indices (HVIs), multiple band-HVIs (MB-HVIs) developed from Sequential Search Methods (SSM), and MB-HVIs developed from Principal Component Regression. Overall, the two-band HVIs and MB-HVIs developed from SSMs using first derivative transformed spectra in the visible blue and green and NIR explained more biomass variability and had lower error than the other approaches or transformations; however a better search criterion needs to be developed in order to reflect the true ability of the two-band HVI approach. Short-Wave Infrared 1 (1000 to 1700 nm) proved less effective, but still important in the final models.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

Marsh canopy leaf area and orientation calculated for improved marsh structure mapping

An approach is presented for producing the spatiotemporal estimation of leaf area index (LAI) of a highly heterogeneous coastal marsh without reliance on user estimates of marsh leaf-stem orientation. The canopy LAI profile derivation used three years of field measured photosynthetically active radiation (PAR) vertical profiles at seven S. alterniflora marsh sites and iterative transform of those PAR attenuation profiles to best-fit light extinction coefficients (KM). KM sun zenith dependency was removed obtaining the leaf angle distribution (LAD) representing the average marsh orientation and the LAD used to calculate the LAI canopy profile. LAI and LAD reproduced measured PAR profiles with 99% accuracy and corresponded to field documented structures. LAI and LAD better reflect marsh structure and results substantiate the need to account for marsh orientation. The structure indexes are directly amenable to remote sensing spatiotemporal mapping and offer a more meaningful representation of wetland systems promoting biophysical function understanding.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

Validation of geometric accuracy of Global Land Survey (GLS) 2000 data

The Global Land Survey (GLS) 2000 data were generated from Geocover™ 2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™ 2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, orthorectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™ 2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

Vegetation burn severity mapping using Landsat-8 and WorldView-2

We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

A landsat data tiling and compositing approach optimized for change detection in the conterminous United States

Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

Valuing geospatial information: Using the contingent valuation method to estimate the economic benefits of Landsat satellite imagery

While the U.S. government does not charge for downloading Landsat images, the images have value to users. This paper demonstrates a method that can value Landsat and other imagery to users. A survey of downloaders of Landsat images found: (a) established US users have a mean value of $912 USD per scene; (b) new US users and users returning when imagery became free have a mean value of $367 USD per scene. Total US user benefits for the 2.38 million scenes downloaded is $1.8 billion USD. While these benefits indicate a high willingness-to-pay among many Landsat downloaders, it would be economically inefficient for the US government to charge for Landsat imagery. Charging a price of $100 USD a scene would result in an efficiency loss of $37.5 million a year. This economic information should be useful to policy-makers who must decide about the future of this and similar remote sensing programs.

Photogrammetric Engineering and Remote Sensing↗
USGS2016

Estimating forest and woodland aboveground biomass using active and passive remote sensing

Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R 2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14 Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1 . Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.

Photogrammetric Engineering and Remote Sensing↗
USGS2016

Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona

Tamarisk is an invasive, riparian shrub species in the southwestern USA. The northern tamarisk beetle (Diorhabda carinulata) has been introduced to several states to control tamarisk. We classified tamarisk distribution in the Glen Canyon National Recreation Area, Arizona using a 0.2 m resolution, airborne multispectral data and estimated tamarisk beetle effects (overall accuracy of 86 percent) leading to leaf defoliation in a 49,408 m2 area. We also estimated individual tamarisk tree biomass and their uncertainties using airbonre liday data (100 points/m2). On average, total above ground tamarisk biomass was 8.67 kg/m2 (SD=17.6). The tamarisk beetle defoliation resulted in a mean leaf biomass loss of 0.52 kg/m2 and an equivalent of 25,692 kg across the entire study area. Our defoliated tamarisk map and biomass estimates can help inform restoration treatments to reduce tamarisk. Continued monitoring of tamarisk and tamarisk beetle effects are recommended to understand the currently-unknown eventual equilibrium between the two species and the cascading effects on ecosystem processes.

Photogrammetric Engineering and Remote Sensing↗
USGS2017

Operational shoreline mapping with high spatial resolution radar and geographic processing

A comprehensive mapping technology was developed utilizing standard image processing and available GIS procedures to automate shoreline identification and mapping from 2 m synthetic aperture radar ( SAR ) HH amplitude data. The development used four NASA Uninhabited Aerial Vehicle SAR ( UAVSAR ) data collections between summer 2009 and 2012 and a fall 2012 collection of wetlands dominantly fronted by vegetated shorelines along the Mississippi River Delta that are beset by severe storms, toxic releases, and relative sea-level rise. In comparison to shorelines interpreted from 0.3 m and 1 m orthophotography, the automated GIS 10 m alongshore sampling found SAR shoreline mapping accuracy to be ±2 m, well within the lower range of reported shoreline mapping accuracies. The high comparability was obtained even though water levels differed between the SAR and photography image pairs and included all shorelines regardless of complexity. The SAR mapping technology is highly repeatable and extendable to other SAR instruments with similar operational functionality.

Photogrammetric Engineering and Remote Sensing↗