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Results for “Advances in Remote Sensing”

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At least 433 records · Page 24Linked to original sources
USGS2011

Mapping three-dimensional surface deformation by combining multiple-aperture interferometry and conventional interferometry: Application to the June 2007 eruption of Kilauea Volcano, Hawaii

Surface deformation caused by an intrusion and small eruption during June 17-19, 2007, along the East Rift Zone of Kilauea Volcano, Hawaii, was three-dimensionally reconstructed from radar interferograms acquired by the Advanced Land Observing Satellite (ALOS) phased-array type L-band synthetic aperture radar (SAR) (PALSAR) instrument. To retrieve the 3-D surface deformation, a method that combines multiple-aperture interferometry (MAI) and conventional interferometric SAR (InSAR) techniques was applied to one ascending and one descending ALOS PALSAR interferometric pair. The maximum displacements as a result of the intrusion and eruption are about 0.8, 2, and 0.7 m in the east, north, and up components, respectively. The radar-measured 3-D surface deformation agrees with GPS data from 24 sites on the volcano, and the root-mean-square errors in the east, north, and up components of the displacement are 1.6, 3.6, and 2.1 cm, respectively. Since a horizontal deformation of more than 1 m was dominantly in the north-northwest-south-southeast direction, a significant improvement of the north-south component measurement was achieved by the inclusion of MAI measurements that can reach a standard deviation of 3.6 cm. A 3-D deformation reconstruction through the combination of conventional InSAR and MAI will allow for better modeling, and hence, a more comprehensive understanding, of the source geometry associated with volcanic, seismic, and other processes that are manifested by surface deformation.

Hawai'i↗
USGS2003

Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data

Evaluation of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the Mountain Pass, California area indicates that several important lithologic groups can be mapped in areas with good exposure by using spectral-matching techniques. The three visible and six near-infrared bands, which have 15-m and 30-m resolution, respectively, were calibrated by using in situ measurements of spectral reflectance. Calcitic rocks were distinguished from dolomitic rocks by using matched-filter processing in which image spectra were used as references for selected spectral categories. Skarn deposits and associated bright coarse marble were mapped in contact metamorphic zones related to intrusion of Mesozoic and Tertiary granodioritic rocks. Fe-muscovite, which is common in these intrusive rocks, was distinguished from Al-muscovite present in granitic gneisses and Mesozoic granite. Quartzose rocks were readily discriminated, and carbonate rocks were mapped as a single broad unit through analysis of the 90-m resolution, five-band surface emissivity data, which is produced as a standard product at the EROS Data Center. Three additional classes resulting from spectral-angle mapper processing ranged from (1) a broad granitic rock class (2) to predominately granodioritic rocks and (3) a more mafic class consisting mainly of mafic gneiss, amphibolite and variable mixtures of carbonate rocks and silicate rocks. ?? 2002 Elsevier Science Inc. All rights reserved.

Remote Sensing of Environment↗
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↗
USGS2003

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989–2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI–SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.

Remote Sensing of Environment↗
USGS2018

Modeling wildfire-induced permafrost deformation in an Alaskan boreal forest using InSAR observations

The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.

Alaska↗
USGS2006

Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data

The Reko Diq, Pakistan mineralized study area, approximately 10??km in diameter, is underlain by a central zone of hydrothermally altered rocks associated with Cu-Au mineralization. The surrounding country rocks are a variable mixture of unaltered volcanic rocks, fluvial deposits, and eolian quartz sand. Analysis of 15-band Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of the study area, aided by laboratory spectral reflectance and spectral emittance measurements of field samples, shows that phyllically altered rocks are laterally extensive, and contain localized areas of argillically altered rocks. In the visible through shortwave-infrared (VNIR + SWIR) phyllically altered rocks are characterized by Al-OH absorption in ASTER band 6 because of molecular vibrations in muscovite, whereas argillically altered rocks have an absorption feature in band 5 resulting from alunite. Propylitically altered rocks form a peripheral zone and are present in scattered exposures within the main altered area. Chlorite and muscovite cause distinctive absorption features at 2.33 and 2.20????m, respectively, although less intense 2.33????m absorption is also present in image spectra of country rocks. Important complementary lithologic information was derived by analysis of the spectral emittance data in the 5 thermal-infrared (TIR) bands. Silicified rocks were not distinguished in the 9 VNIR + SWIR bands because of the lack of diagnostic spectral absorption features in quartz in this wavelength region. Quartz-bearing surficial deposits, as well as hydrothermally silicified rocks, were mapped in the TIR bands by using a band 13/band 12 ratio image, which is sensitive to the intensity of the quartz reststrahlen feature. Improved distinction between the quartzose surficial deposits and silicified bedrock was achieved by using matched-filter processing with TIR image spectra for reference. ?? 2006 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2009

Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack

Tamarisk (Tamarix spp.) has invaded riparian ecosystems throughout the Western United States, including significant portions of riparian ecosystems within U.S. National Parks and Monuments. Recently, the saltcedar leaf beetle (Diorhabda elongata) was released as a tamarisk biocontrol agent. Although initial releases have been monitored, no comprehensive program is currently in place to monitor the rapid spread of Diorhabda that has resulted from numerous subsequent releases by county and state agencies. Long term monitoring of tamarisk defoliation and its impacts on habitat and water resources is needed. This study examines the potential for using higher spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and lower spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data for monitoring defoliation caused by Diorhabda and subsequent changes in evapotranspiration (ET). Widespread tamarisk defoliation was observed in an eastern Utah study area during summer 2007. ASTER normalized difference vegetation index (NDVI) showed only minor changes between 2005 and 2006, but a significant drop in NDVI was found within riparian areas between 2006 and 2007. The decrease in NDVI caused by defoliation was apparent despite partial refoliation within the study area. MODIS time series data revealed that absolute decline in EVI varied by site, but that the timing of EVI decline during summer 2007 was early with respect to phenological patterns from 2001 through 2006. Defoliation caused decreases in ET values estimated from both ASTER and MODIS data. MODIS estimated ET declined earlier than in previous years, although annual ET was not significantly different than ET in previous years due to high year-to-year variability. Challenges to detection and monitoring of tamarisk defoliation include spectral mixing of tamarisk and other cover types at subpixel spatial resolution, spatial coregistration of time series images, the timing of image acquisition, and changes unrelated to defoliation in non-tamarisk land cover over time. Continued development of the techniques presented in this paper may allow monitoring the spread of Diorhabda and assessment of potential water salvage resulting from biocontrol of tamarisk. ?? 2009 Elsevier Inc.

Remote Sensing of Environment↗
USGS2026

Advances in volcano monitoring driven by the first decade of Sentinel-1 observations

Sentinel-1 has transformed how satellite radar data (SAR and InSAR) are used in volcanology. The systematic, long-term archive and open-access policy means that volcano observatories and research organisations have invested in integrating Sentinel-1 datasets into their monitoring systems. We identify 233 high priority volcanoes and estimate that Sentinel-1 data has been used in peer-reviewed publications for 90 of them. We examine a global archive of 3.3 million automatically processed Sentinel-1 interferograms of volcanoes and use machine learning methods to identify eruptions and periods of unrest. We then review the ways in which InSAR data are being used in different contexts. At frequently erupting basaltic systems in Iceland, Hawaiʻi, the Galápagos , and Piton de la Fournaise, InSAR has become an effective monitoring tool and is integrated with other datasets and models to forecast magma pathways. For large explosive eruptions, deformation measurements often remain challenging, but SAR backscatter is increasingly used to map damaging flows and measure the changing shape of ocean islands. Sentinel-1's long archive provides critical baseline measurements that are vital for measuring slow deformation, capturing new periods of unrest and providing fresh insights into subsurface dynamics. Understanding the drivers of deformation remains challenging and typically relies on integration with external datasets. Future European Space Agency missions have the potential to improve both resolution and coverage providing an even richer dataset to further enhance global volcano monitoring

Erta Ale volcano↗
USGS2005

Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

Spectral measurements made in the Mordor Pound, NT, Australia study area using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), in the laboratory and in situ show dominantly Al-OH and ferric-iron VNIR-SWIR absorption features in felsic rock spectra and ferrous-iron and Fe,Mg-OH features in the mafic-ultramafic rock spectra. ASTER ratio images, matched-filter, and spectral-angle mapper processing (SAM) were evaluated for mapping the lithologies. Matched-filter processing in which VNIR + SWIR image spectra were used for reference resulted in 4 felsic classes and 4 mafic-ultramafic classes based on Al-OH or Fe,Mg-OH absorption features and, in some, subtle reflectance differences related to differential weathering and vegetation. These results were similar to those obtained by match-filter analysis of HyMap data from a previous study, but the units were more clearly demarcated in the HyMap image. ASTER TIR spectral emittance data and laboratory emissivity measurements document a wide wavelength range of Si-O spectral features, which reflect the lithological diversity of the Mordor ultramafic complex and adjacent rocks. SAM processing of the spectral emittance data distinguished 2 classes representing the mafic-ultramafic rocks and 4 classes comprising the quartzose to intermediate composition rocks. Utilization of the complementary attributes of the spectral reflectance and spectral emittance data resulted in discrimination of 4 mafic-ultramafic categories; 3 categories of alluvial-colluvial deposits; and a significantly more completely mapped quartzite unit than could be accomplished by using either data set alone. ?? 2005 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2021

Advancing cave detection using terrain analysis and thermal imagery

Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.

Remote Sensing↗
USGS2011

Monitoring duration and extent of storm-surge and flooding in Western Coastal Louisiana marshes with Envisat ASAR data

Inundation maps of coastal marshes in western Louisiana were created with multitemporal Envisat Advanced Synthetic Aperture (ASAR) scenes collected before and during the three months after Hurricane Rita landfall in September 2005. Corroborated by inland water-levels, 7 days after landfall, 48% of coastal estuarine and palustrine marshes remained inundated by storm-surge waters. Forty-five days after landfall, storm-surge inundated 20% of those marshes. The end of the storm-surge flooding was marked by an abrupt decrease in water levels following the passage of a storm front and persistent offshore winds. A complementary dramatic decrease in flood extent was confirmed by an ASAR-derived inundation map. In nonimpounded marshes at elevations <;80 cm, storm-surge waters rapidly receded while slower recession was dominantly associated with impounded marshes at elevations >;80 cm during the first month after Rita landfall. After this initial period, drainage from marshes-especially impounded marshes-was hastened by the onset of offshore winds. Following the abrupt drops in inland water levels and flood extent, rainfall events coinciding with increased water levels were recorded as inundation re-expansion. This postsurge flooding decreased until only isolated impounded and palustrine marshes remained inundated. Changing flood extents were correlated to inland water levels and largely occurred within the same marsh regions. Trends related to incremental threshold increases used in the ASAR change-detection analyses seemed related to the preceding hydraulic and hydrologic events, and VV and HH threshold differences supported their relationship to the overall wetland hydraulic condition.

Louisiana↗
USGS2026

Comparing DESIS hyperspectral and Landsat 10 simulated superspectral data for crop type classification in California's Central Valley

To advance crop type mapping in support of global food and water security, this study compared three spectral configurations: (A) the full 60-band DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral narrowband (HNB) dataset, (B) a 14-band subset of DESIS-derived HNBs aligned with the planned Landsat 10 (formerly Landsat Next) spectral configuration (400–1000 nm), and (C) DESIS-based simulations of Landsat 10 superspectral broadbands. The analysis was conducted in California’s Central Valley, hereafter referred to as “the Central Valley”, during the peak growing month of August. DESIS imagery from August 2021, 2022, and 2023 was used sequentially for model development, testing, and independent validation. Over these three years, DESIS provided extensive hyperspectral coverage of much of the 4 million hectares in the Central Valley’s. Analyses were performed on Google Earth Engine using two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), to differentiate three major crop classes: row crops, grapes and tree crops, and winter wheat/fallow/other. The highest overall accuracy (86%) was achieved using SVM in combination with either the full DESIS hyperspectral dataset or the 14 DESIS narrowbands corresponding to Landsat 10. This finding aligns with earlier studies showing a small number of strategically positioned narrowbands can be optimal for crop type classification. Use of the narrowband datasets resulted in substantially higher accuracy (overall accuracy of 86%) compared to the simulated Landsat 10 broadbands (overall accuracy of 75%), supporting previous studies highlighting the utility of narrowbands. Despite the high accuracy using August imagery, the study indicates more granular crop type classification will require multi-temporal observations spanning the full phenological cycle (June–October), especially for a large number of crop classes. Acquiring task-based hyperspectral imagery over such large areas throughout the growing season remains operationally challenging. In contrast, Landsat 10 superspectral imagery could provide routine coverage across seasons and years that is practical and scalable for future large area crop type mapping and agricultural monitoring.

California↗
USGS1998

ASTER preflight and inflight calibration and the validation of level 2 products

Describes the preflight and inflight calibration approaches used for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The system is a multispectral, high-spatial resolution sensor on the Earth Observing System's EOS-AM1 platform. Preflight calibration of ASTER uses well-characterized sources to provide calibration and preflight round-robin exercises to understand biases between the calibration sources of ASTER and other EOS sensors. These round-robins rely on well-characterized, ultra-stable radiometers. An experiment field in Yokohama, Japan, showed that the output from the source used for the visible and near-infrared (VNIR) subsystem of ASTER may be underestimated by 1.5%, but this is still within the 4% specification for the absolute, radiometric calibration of these bands. Inflight calibration will rely on vicarious techniques and onboard blackbodies and lamps. Vicarious techniques include ground-reference methods using desert and water sites. A recent joint field campaign gives confidence that these methods currently provide absolute calibration to better than 5%, and indications are that uncertainties less than the required 4% should be achievable at launch. The EOS-AM1 platform will also provide a spacecraft maneuver that will allow ASTER to see the Moon, allowing further characterization of the sensor. A method for combining the results of these independent calibration results is presented. The paper also describes the plans for validating the Level 2 data products from ASTER. These plans rely heavily upon field campaigns using methods similar to those used for the ground-reference, vicarious calibration methods.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2010

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

International Journal of Remote Sensing↗
USGS2021

Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.

Alaska↗
USGS2007

Compression of the Global Land 1-km AVHRR dataset

Large datasets, such as the Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) Data Set (Eidenshink and Faundeen 1994), require compression methods that provide efficient storage and quick access to portions of the data. A method of lossless compression is described that provides multiresolution decompression within geographic subwindows of multi-spectral, global, 1-km, AVHRR images. The compression algorithm segments each image into blocks and compresses each block in a hierarchical format. Users can access the data by specifying either a geographic subwindow or the whole image and a resolution (1,2,4, 8, or 16 km). The Global Land 1-km AVHRR data are presented in the Interrupted Goode's Homolosine map projection. These images contain masked regions for non-land areas which comprise 80 per cent of the image. A quadtree algorithm is used to compress the masked regions. The compressed region data are stored separately from the compressed land data. Results show that the masked regions compress to 0·143 per cent of the bytes they occupy in the test image and the land areas are compressed to 33·2 per cent of their original size. The entire image is compressed hierarchically to 6·72 per cent of the original image size, reducing the data from 9·05 gigabytes to 623 megabytes. These results are compared to the first order entropy of the residual image produced with lossless Joint Photographic Experts Group predictors. Compression results are also given for Lempel-Ziv-Welch (LZW) and LZ77, the algorithms used by UNIX compress and GZIP respectively. In addition to providing multiresolution decompression of geographic subwindows of the data, the hierarchical approach and the use of quadtrees for storing the masked regions gives a marked improvement over these popular methods.

International Journal of Remote Sensing↗
USGS2022

Absolute accuracy assessment of lidar point cloud using amorphous objects

The accuracy assessment of airborne lidar point cloud typically estimates vertical accuracy by computing RMSEz (root mean square error of the z coordinate) from ground check points (GCPs). Due to the low point density of the airborne lidar point cloud, there is often not enough accurate semantic context to find an accurate conjugate point. To advance the accuracy assessment in full three-dimensional (3D) context, geometric features, such as the three-plane intersection point or two-line intersection point, are often used. Although the point density is still low, geometric features are mathematically modeled from many points. Thus, geometric features provide a robust determination of the intersection point, and the point is considered as a GCP. When no regular built objects are available, we describe the process of utilizing features of irregular shape called amorphous natural objects, such as a tree or a rock. When scanned to a high-density point cloud, an amorphous natural object can be used as ground truth reference data to estimate 3D georeferencing errors of the airborne lidar point cloud. The algorithm to estimate 3D accuracy is the optimization that minimizes the sum of the distance between the airborne lidar points to the ground scanned data. The search volume partitioning was the most important procedure to improve the computational efficiency. We also performed an extensive study to address the external uncertainty associated with the amorphous object method. We describe an accuracy assessment using amorphous objects (108 trees) spread over the project area. The accuracy results for ∆ x , ∆ y , and ∆ z obtained using the amorphous object method were 3.1 cm, 3.6 cm, and 1.7 cm RMSE, along with a mean error of 0.1 cm, 0.1 cm, and 4.5 cm, respectively, satisfying the accuracy requirement of U.S. Geological Survey lidar base specification. This approach shows strong promise as an alternative to geometric feature methods when artificial targets are scarce. The relative convenience and advantages of using amorphous targets, along with its good performance shown here, make this amorphous object method a practical way to perform 3D accuracy assessment.

Remote Sensing↗
USGS2013

Radiometric cross-calibration of EO-1 ALI with L7 ETM+ and Terra MODIS sensors using near-simultaneous desert observations

The Earth Observing-1 (EO-1) satellite was launched on November 21, 2000, as part of a one-year technology demonstration mission. The mission was extended because of the value it continued to add to the scientific community. EO-1 has now been operational for more than a decade, providing both multispectral and hyperspectral measurements. As part of the EO-1 mission, the Advanced Land Imager (ALI) sensor demonstrates a potential technological direction for the next generation of Landsat sensors. To evaluate the ALI sensor capabilities as a precursor to the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission (LDCM, or Landsat 8 after launch), its measured top-of-atmosphere (TOA) reflectances were compared to the well-calibrated Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors in the reflective solar bands (RSB). These three satellites operate in a near-polar, sun-synchronous orbit 705 km above the Earth's surface. EO-1 was designed to fly one minute behind L7 and approximately 30 minutes in front of Terra. In this configuration, all the three sensors can view near-identical ground targets with similar atmospheric, solar, and viewing conditions. However, because of the differences in the relative spectral response (RSR), the measured physical quantities can be significantly different while observing the same target. The cross-calibration of ALI with ETM+ and MODIS was performed using near-simultaneous surface observations based on image statistics from areas observed by these sensors over four desert sites (Libya 4, Mauritania 2, Arabia 1, and Sudan 1). The differences in the measured TOA reflectances due to RSR mismatches were compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of each sensor. For this study, the spectral profile of the target comes from the near-simultaneous EO-1 Hyperion data over these sites. The results indicate that the TOA reflectance measurements for ALI agree with those of ETM+ and MODIS to within 5% after the application of SBAF.

Arabia↗