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At least 127 records · Page 7Linked to original sources
USGS2014

Land cover mapping of Greater Mesoamerica using MODIS data

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: ( i ) qualitative assessment provided good insight in identifying and correcting gross errors; ( ii ) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest ( r 2  = 0.88), shrubland ( r 2  = 0.75), and cropland ( r 2  = 0.97) but weak positive association for grassland ( r 2  = 0.26); and ( iii ) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Canadian Journal of Remote Sensing↗
USGS2014

InSAR detects possible thaw settlement in the Alaskan Arctic Coastal Plain

Satellite interferometric synthetic aperture radar (InSAR) has proven to be an effective tool for monitoring surface deformation from volcanoes, earthquakes, landslides, and groundwater withdrawal. This paper seeks to expand the list of applications of InSAR data to include monitoring subsidence possibly associated with thaw settlement over the Alaskan Arctic Coastal Plain. To test our hypothesis that InSAR data are sufficiently sensitive to detect subsidence associated with thaw settlement, we acquired all Japanese Earth Resources Satellite-1 (JERS-1) L-band data available for the summers of 1996, 1997, and 1998 over two sites on the Alaska North Slope. The least amount of subsidence for both study sites was detected in the interferograms covering the summer of 1996 (2-3 cm), interferograms from 1997 and 1998 revealed that about 3 cm of subsidence occurred at the northern Cache One Lake site, and about 5 cm of subsidence was detected at the southern Kaparuk River site. These preliminary results illustrate the capacity of the L-band (24 cm) wavelength JERS-1 radar data to penetrate the short Arctic vegetation to monitor subsidence possibly associated with thaw settlement of the active layer and (or) other hydrologic changes over relatively large areas.

Canadian Journal of Remote Sensing↗
USGS2014

Evaluation of Landsat-7 SLC-off image products for forest change detection

Since July 2003, Landsat-7 ETM+ has been operating without the scan line corrector (SLC), which compensates for the forward motion of the satellite in the imagery acquired. Data collected in SLC-off mode have gaps in a systematic wedge-shaped pattern outside of the central 22 km swath of the imagery; however, the spatial and spectral quality of the remaining portions of the imagery are not diminished. To explore the continued use of Landsat-7 ETM+ SLC-off imagery to characterize change in forested environments, we compare the change detection results generated from a reference image pair (a 1999 Landsat-7 ETM+ image and a 2003 Landsat-5 TM image) with change detection results generated from the same 1999 Landsat-7 ETM+ image coupled with three different 2003 Landsat-7 SLC-off products: unremediated SLC-off (i.e., with gaps); histogram-based gap-filled; and segment-based gap-filled. The results are compared on both a pixel and polygon basis; on a pixel basis, the unremediated SLC-off product missed 35% of the change identified by the reference data, and the histogram- and segment-based gap-filled products missed 23% and 21% of the change, respectively. When using forest inventory polygons as a context for change (to reduce commission error), the amount of change missed was 31%, 14%, and 12% for the each of the unremediated, histogram-based gap-filled, and segment-based gap-filled products, respectively. Our results indicate that over the time period considered, and given the types and spatial distribution of change events within our study area, the gap-filled products can provide a useful data source for change detection in forested environments. The selection of which product to use is, however, very dependent on the nature of the application and the spatial configuration of change events. ?? 2008 Government of Canada.

Canadian Journal of Remote Sensing↗
USGS2014

Establishing the Antarctic Dome C community reference standard site towards consistent measurements from Earth observation satellites

Establishing satellite measurement consistency by using common desert sites has become increasingly more important not only for climate change detection but also for quantitative retrievals of geophysical variables in satellite applications. Using the Antarctic Dome C site (75°06′S, 123°21′E, elevation 3.2 km) for satellite radiometric calibration and validation (Cal/Val) is of great interest owing to its unique location and characteristics. The site surface is covered with uniformly distributed permanent snow, and the atmospheric effect is small and relatively constant. In this study, the long-term stability and spectral characteristics of this site are evaluated using well-calibrated satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Preliminary results show that despite a few limitations, the site in general is stable in the long term, the bidirectional reflectance distribution function (BRDF) model works well, and the site is most suitable for the Cal/Val of reflective solar bands in the 0.4–1.0 µm range. It was found that for the past decade, the reflectivity change of the site is within 1.35% at 0.64 µm, and interannual variability is within 2%. The site is able to resolve calibration biases between instruments at a level of ~1%. The usefulness of the site is demonstrated by comparing observations from seven satellite instruments involving four space agencies, including OrbView-2–SeaWiFS, Terra–Aqua MODIS, Earth Observing 1 (EO-1) – Hyperion, Meteorological Operational satellite programme (MetOp) – Advanced Very High Resolution Radiometer (AVHRR), Envisat Medium Resolution Imaging Spectrometer (MERIS) – dvanced Along-Track Scanning Radiometer (AATSR), and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Dome C is a promising candidate site for climate quality calibration of satellite radiometers towards more consistent satellite measurements, as part of the framework for climate change detection and data quality assurance for the Global Earth Observation System of Systems (GEOSS).

Canadian Journal of Remote Sensing↗
USGS2012

Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery

Programs to monitor lake area change are becoming increasingly important in high latitude regions, and their development often requires evaluating tradeoffs among different approaches in terms of accuracy of measurement, consistency across multiple users over long time periods, and efficiency. We compared three supervised methods for lake classification from Landsat imagery (density slicing, classification trees, and feature extraction). The accuracy of lake area and number estimates was evaluated relative to high-resolution aerial photography acquired within two days of satellite overpasses. The shortwave infrared band 5 was better at separating surface water from nonwater when used alone than when combined with other spectral bands. The simplest of the three methods, density slicing, performed best overall. The classification tree method resulted in the most omission errors (approx. 2x), feature extraction resulted in the most commission errors (approx. 4x), and density slicing had the least directional bias (approx. half of the lakes with overestimated area and half of the lakes with underestimated area). Feature extraction was the least consistent across training sets (i.e., large standard error among different training sets). Density slicing was the best of the three at classifying small lakes as evidenced by its lower optimal minimum lake size criterion of 5850 m 2 compared with the other methods (8550 m 2 ). Contrary to conventional wisdom, the use of additional spectral bands and a more sophisticated method not only required additional processing effort but also had a cost in terms of the accuracy and consistency of lake classifications.

Canadian Journal of Remote Sensing↗
USGS2008

Soil sail content estimation in the Yellow River delta with satellite hyperspectral data

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl 2 . Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487–1527, 1971–1991, 2032–2092, and 2163–2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSE C = 0.753 ( R 2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSE V = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 ( R 2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area.

Canadian Journal of Remote Sensing↗
USGS1978

EROS Data Center Landsat digital enhancement techniques and imagery availability

The US Geological Survey's EROS Data Center (EDC) is experimenting with the production of digitally enhanced Landsat imagery. Advanced digital image processing techniques are used to perform geometric and radiometric corrections and to perform contrast and edge enhancements. The enhanced image product is produced from digitally preprocessed Landsat computer compatible tapes (CCTs) on a laser beam film recording system. Landsat CCT data have several geometric distortions which are corrected when NASA produces the standard film products. When producing film images from CCT's, geometric correction of the data is required. The EDC Digital Image Enhancement System (EDIES) compensates for geometric distortions introduced by Earth's rotation, variable line length, non-uniform mirror scan velocity, and detector misregistration. Radiometric anomalies such as bad data lines and striping are common to many Landsat film products and are also in the CCT data. Bad data lines or line segments with more than 150 contiguous bad pixels are corrected by inserting data from the previous line in place of the bad data. Striping, caused by variations in detector gain and offset, is removed with a destriping algorithm applied after digitally enhancing the data. Image enhancement is performed by applying a linear contrast stretch and an edge enhancement algorithm. The linear contrast enhancement algorithm is designed to expand digitally the full range of useful data recorded on the CCT over the range of 256 digital counts. This minimizes the effect of atmospheric scattering and saturates the relative brightness of highly reflecting features such as clouds or snow. It is the intent that no meaningful terrain data are eliminated by the digital processing. The edge enhancement algorithm is designed to enhance boundaries between terrain features that exhibit subtle differences in brightness values along edges of features. After the digital data have been processed, data for each Landsat band are recorded on black-and-white film with a laser beam film recorder (LBR). The LBR corrects for aspect ratio distortions as the digital data are recorded on the recording film over a preselected density range. Positive transparencies of MSS bands 4, 5, and 7 produced by the LBR are used to make color composite transparencies. Color film positives are made photographically from first generation black-and-white products generated on the LBR.

Canadian Journal of Remote Sensing↗
USGS1979

Inventory and mapping of flood inundation using interactive digital image analysis techniques

LANDSAT digital data and color infra-red photographs were used in a multiphase sampling scheme to estimate the area of agricultural land affected by a flood. The LANDSAT data were classified with a maximum likelihood algorithm. Stratification of the LANDSAT data, prior to classification, greatly reduced misclassification errors. The classification results were used to prepare a map overlay showing the areal extent of flooding. These data also provided statistics required to estimate sample size in a two phase sampling scheme, and provided quick, accurate estimates of areas flooded for the first phase. The measurements made in the second phase, based on ground data and photo-interpretation, were used with two phase sampling statistics to estimate the area of agricultural land affected by flooding These results show that LANDSAT digital data can be used to prepare map overlays showing the extent of flooding on agricultural land and, with two phase sampling procedures, can provide acreage estimates with sampling errors of about 5 percent. This procedure provides a technique for rapidly assessing the areal extent of flood conditions on agricultural land and would provide a basis for designing a sampling framework to estimate the impact of flooding on crop production.

Canadian Journal of Remote Sensing↗
USGS2009

Distributed geospatial model sharing based on open interoperability standards

Numerous geospatial computational models have been developed based on sound principles and published in journals or presented in conferences. However modelers have made few advances in the development of computable modules that facilitate sharing during model development or utilization. Constraints hampering development of model sharing technology includes limitations on computing, storage, and connectivity; traditional stand-alone and closed network systems cannot fully support sharing and integrating geospatial models. To address this need, we have identified methods for sharing geospatial computational models using Service Oriented Architecture (SOA) techniques and open geospatial standards. The service-oriented model sharing service is accessible using any tools or systems compliant with open geospatial standards, making it possible to utilize vast scientific resources available from around the world to solve highly sophisticated application problems. The methods also allow model services to be empowered by diverse computational devices and technologies, such as portable devices and GRID computing infrastructures. Based on the generic and abstract operations and data structures required for Web Processing Service (WPS) standards, we developed an interactive interface for model sharing to help reduce interoperability problems for model use. Geospatial computational models are shared on model services, where the computational processes provided by models can be accessed through tools and systems compliant with WPS. We developed a platform to help modelers publish individual models in a simplified and efficient way. Finally, we illustrate our technique using wetland hydrological models we developed for the prairie pothole region of North America.

Journal of Remote Sensing↗
USGS2019

Optical characterization of two cyanobacteria genera, Aphanizomenon and Microcystis, with hyperspectral microscopy.

Cyanobacterial blooms are a nuisance and a potential hazard in freshwater systems worldwide. Remote sensing has been used to detect cyanobacterial blooms, but few studies have distinguished between genera of cyanobacteria. Because some genera are more likely to be toxic than others, this is a useful distinction.In this study, hyperspectral imaging reflectance microscopy was used to examine cyanobacteria from Upper Klamath Lake, Oregon, USA, at high spatial and spectral resolution to determine if two species found commonly in the lake, Aphanizomenon flos-aquae and Microcystis aeruginosa, can be separated spectrally. Of the analytical methods applied, a spectral shape algorithm applied to the derivative was found to be most successful in classifying these species in microscope scenes. Further work is required to determine if the spectral characterization of cyanobacterial genera can be scaled up to other remote sensing applications.

Oregon↗
USGS2007

FFT-enhanced IHS transform method for fusing high-resolution satellite images

Existing image fusion techniques such as the intensity-hue-saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. ?? 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2010

Irrigated areas of India derived using MODIS 500 m time series for the years 2001-2003

The overarching goal of this research was to develop methods and protocols for mapping irrigated areas using a Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m time series, to generate irrigated area statistics, and to compare these with ground- and census-based statistics. The primary mega-file data-cube (MFDC), comparable to a hyper-spectral data cube, used in this study consisted of 952 bands of data in a single file that were derived from MODIS 500 m, 7-band reflectance data acquired every 8-days during 2001-2003. The methods consisted of (a) segmenting the 952-band MFDC based not only on elevation-precipitation-temperature zones but on major and minor irrigated command area boundaries obtained from India's Central Board of Irrigation and Power (CBIP), (b) developing a large ideal spectral data bank (ISDB) of irrigated areas for India, (c) adopting quantitative spectral matching techniques (SMTs) such as the spectral correlation similarity (SCS) R 2 -value, (d) establishing a comprehensive set of protocols for class identification and labeling, and (e) comparing the results with the National Census data of India and field-plot data gathered during this project for determining accuracies, uncertainties and errors. The study produced irrigated area maps and statistics of India at the national and the subnational (e.g., state, district) levels based on MODIS data from 2001-2003. The Total Area Available for Irrigation (TAAI) and Annualized Irrigated Areas (AIAs) were 113 and 147 million hectares (MHa), respectively. The TAAI does not consider the intensity of irrigation, and its nearest equivalent is the net irrigated areas in the Indian National Statistics. The AIA considers intensity of irrigation and is the equivalent of "irrigated potential utilized (IPU)" reported by India's Ministry of Water Resources (MoWR). The field-plot data collected during this project showed that the accuracy of TAAI classes was 88% with a 12% error of omission and 32% of error of commission. Comparisons between the AIA and IPU produced an R 2 -value of 0.84. However, AIA was consistently higher than IPU. The causes for differences were both in traditional approaches and remote sensing. The causes of uncertainties unique to traditional approaches were (a) inadequate accounting of minor irrigation (groundwater, small reservoirs and tanks), (b) unwillingness to share irrigated area statistics by the individual Indian states because of their stakes, (c) absence of comprehensive statistical analyses of reported data, and (d) subjectivity involved in observation-based data collection process. The causes of uncertainties unique to remote sensing approaches were (a) irrigated area fraction estimate and related sub-pixel area computations and (b) resolution of the imagery. The causes of uncertainties common in both traditional and remote sensing approaches were definitions and methodological issues. ?? 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2023

Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m 3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).

Journal of Applied Remote Sensing↗
USGS2013

Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields

Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.

Wyoming↗
USGS2015

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Madre de Dios↗
USGS2014

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

California↗