Four years of Landsat-7 on-orbit geometric calibration and performance

Lee, D.S.; Storey, James C.; Choate, Michael J.; Hayes, R.W.

doi:10.1109/tgrs.2004.836769

Cited by 156 publications

(92 citation statements)

References 2 publications

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“…deforested prior to 2003, pink tones in Figure 3) could not be used as training data. The geolocation error between the PRODES and MODIS datasets can be considered negligible compared to the uncertainty in the position of the active fires within a MODIS pixel -the MODIS accuracy is within 50 m at nadir (Wolfe et al 2002), and although the PRODES accuracy is unknown, it is likely to be comparable to the 30 m pixel level Landsat accuracy (Lee et al 2004). This training data selection approach provides pure fire type training data that is internally consistent with respect to the PRODES classification scheme used by the Brazilian government for monitoring deforestation in the legal amazon.…”

Section: Training Data Definitionmentioning

confidence: 99%

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Roy

Kumar

2016

International Journal of Digital Earth

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The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km 2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user's and producer's classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature. ARTICLE HISTORY

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Section: Training Data Definitionmentioning

confidence: 99%

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Roy

Kumar

2016

International Journal of Digital Earth

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“…Diesel particulate emissions consist of carbonaceous material, generally 75% elemental carbon (EC) known as 'soot' and 20% organic carbon (OC) 21 . The elemental fractions are generated from diesel fuel droplet during pyrolysis, whereas…”

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confidence: 99%

Identification of Potential Glacial Lake Sites and Mapping Maximum Extent of Existing Glacier Lakes in Drang Drung and Samudra Tapu Glaciers, Indian Himalaya

et al. 2016

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“…Each tile is 5000 × 5000 pixels in size, equal to 150 × 150 km (Homer et al, 2004). The Landsat sensor series L1T image geolocation error in the CONUS, including areas with substantial terrain relief, is less than 30 m (< 1 pixel) (Lee, Storey, Choate, & Hayes, 2004). The most recent Landsat data radiometric Cal expertise is employed in the WELD workflow to ensure harmonization and interoperability of multi-sensor Landsat image time-series, with a 5% absolute reflective band Cal uncertainty (Markham & Helder, 2012), in agreement with the GEO-CEOS QA4EO Cal/Val requirements (GEO-CEOS, 2010).…”

Section: Methodsmentioning

confidence: 99%

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

et al. 2018

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ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

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Four years of Landsat-7 on-orbit geometric calibration and performance

Cited by 156 publications

References 2 publications

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Identification of Potential Glacial Lake Sites and Mapping Maximum Extent of Existing Glacier Lakes in Drang Drung and Samudra Tapu Glaciers, Indian Himalaya

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

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