Validation of Geometric Accuracy of Global Land Survey (GLS) 2000 Data

Rengarajan, Rajagopalan; Sampath, Aparajithan; Storey, James C.; Choate, Michael J.

doi:10.14358/pers.81.2.131

Cited by 26 publications

(18 citation statements)

References 10 publications

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“…To derive an estimate of the expected Sentinel-2 to Landsat-8 misregistration, the GLS accuracy specification of 25 meter radial rootmean-squared error [Rengarajan et al, 2015] was converted to the equivalent 2σ value of 35 m (by multiplying by the square root of 2). The root-sum-square of this result with the observed Sentinel-2 accuracy (without GRI ground control) of 14.6 m (2σ) [ESA, 2016] yields a 38 meter (2σ) expected registration accuracy between the sensors.…”

Section: Expected Misregistration Magnitudementioning

confidence: 99%

A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery

Storey

Roy

Masek

et al. 2016

Remote Sensing of Environment

Self Cite

146

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The Landsat-8 and Sentinel-2 sensors provide multi-spectral image data with similar spectral and spatial characteristics that together provide improved temporal coverage globally. Both systems are designed to register Level 1 products to a reference image framework, however, the Landsat-8 framework, based upon the Global Land Survey images, contains residual geolocation errors leading to an expected sensor-to-sensor misregistration of 38 m (2σ). These misalignments vary geographically but should be stable for a given area. The Landsat framework will be readjusted for consistency with the Sentinel-2 Global Reference Image, with completion expected in 2018. In the interim, users can measure Landsat-to-Sentinel tie points to quantify the misalignment in their area of interest and if appropriate to reproject the data to better alignment.

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Section: Expected Misregistration Magnitudementioning

confidence: 99%

A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery

Storey

Roy

Masek

et al. 2016

Remote Sensing of Environment

Self Cite

146

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“…As a result, the majority of the fused datasets are of 3-arc-second resolution, which is consistent with the previously released SRTM3 data. Some representative works include the collection of Viewfinder Panorama (VFP) DEMs [47], the Global Land Survey Digital Elevation Model (GLSDEM) [20], the EarthEnv-DEM90 [19] and the MERIT DEM [21]. The detailed information of the representative fused DEM datasets introduced in this section were given in Table 1.…”

Section: Other Fused Dem Datasetsmentioning

confidence: 99%

“…Thus, considering the auxiliary information among the multi-source datasets, merging the currently available datasets through multi-source data fusion is a possible way to solve this problem. Different fusion techniques were developed to solve the quality issues of DEM data, such as voids, anomalies and different sources of noise [14,[19][20][21][22][23][24]. Based on the fusion methods, great efforts have been made to generate high-quality DEM products by merging the existing datasets [19,21,25].In this paper, we introduce a fused DEM dataset (82°S-82°N) mainly reconstructed from the 30-m unfilled SRTM1, AW3D30, ASTER GDEM v2 and ICESat GLAS data, which we refer to as GSDEM-30.…”

mentioning

confidence: 99%

A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation

L¹,

Shen²,

Liu³

et al. 2019

Preprint

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The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.interferometric signal and stereo images, as well as the post-processing methods, will affect the data accuracy [10].Consequently, the tradeoff between the spatial resolution, spatial coverage, and vertical accuracy means that the currently available datasets do not meet the demands for the large-scale geospatial applications [11]. On the one hand, more advanced imaging and processing technologies will be applied to Earth observation [12], and new products with a higher resolution and fine-edited quality are being generated and released. However, new data generation is not only highly cost and time-consuming, but is also inevitably limited by the imaging techniques [13]. Moreover, the editing of raw DEM data only using the single-source information, e.g. the noise filtering and voids interpolation methods, might fail in the cases where local data quality are poor [14][15][16][17][18]. Thus, considering the auxiliary information among the multi-source datasets, merging the currently available datasets through multi-source data fusion is a possible way to solve this problem. Different fusion techniques were developed to solve the quality issues of DEM data, such as voids, anomalies and different sources of noise [14,[19][20][21][22][23][24]. Based on the fusion methods, great efforts have been made to generate high-quality DEM products by merging the existing datasets [19,21,25].In this paper, we introduce a fused DEM dataset (82°S-82°N) mainly reconstructed from the 30-m unfilled SRTM1, AW3D30, ASTER GDEM v2 and ICESat GLAS data, which we refer to as GSDEM-30. The goal is to generate a global seamless DEM using the multi-source accuracy enhancement method and the regularized multi-scale fusion algorithm [22,26]. Considering the global data characteristics, the artificial neural networ...

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“…The Level-1 product from LPGS is consistent with all standard Level-1 data products created using Landsat 1 to Landsat 7 data. A rigorous orthorectification process is conducted by incorporating ground control points and Digital Elevation Models (DEM) from the GLS2000 data set [12]. The geographic extent of each scene is slightly different, depending on the coverage of the WRS grid.…”

Section: Landsat-8 Automatic Image Processing Systemmentioning

confidence: 99%

Near Real-Time Browsable Landsat-8 Imagery

Liu

Nakamura

et al. 2017

Remote Sensing

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Abstract:The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world on a routine basis. With a size of approximately 1 Gb per scene, however, the standard level-1 product provided by these stations is not able to serve the general public. Users would like to browse the most up-to-date and historical images of their regions of interest (ROI) at full-resolution from all kinds of devices without the need for tedious data downloading, decompressing, and processing. This paper reports on the Landsat-8 automatic image processing system (L-8 AIPS) that incorporates the function of mask developed by United States Geological Survey (USGS), the pan-sharpening technique of spectral summation intensity modulation, the adaptive contrast enhancement technique, as well as the Openlayers and Google Maps/Earth compatible superoverlay technique. Operation of L-8 AIPS enables the most up-to-date Landsat-8 images of Taiwan to be browsed with a clear contrast enhancement regardless of the cloud condition, and in only one hour's time after receiving the raw data from the USGS Level 1 Product Generation System (LPGS). For any ROI in Taiwan, all historical Landsat-8 images can also be quickly viewed in time series at full resolution (15 m). The debris flow triggered by Typhoon Soudelor (8 August 2015), as well as the barrier lake formed and the large-scale destruction of vegetation after Typhoon Nepartak (7 July 2016), are given as three examples of successful applications to demonstrate that the gap between the user's needs and the existing Level-1 product from LPGS can be bridged by providing browsable images in near real-time.

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Validation of Geometric Accuracy of Global Land Survey (GLS) 2000 Data

Cited by 26 publications

References 10 publications

A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery

A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery

A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation

Near Real-Time Browsable Landsat-8 Imagery

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