The 1 km AVHRR global land data set: first stages in implementation

Eidenshink, Jeffery C.; Faundeen, John L.

doi:10.1080/01431169408954339

Cited by 293 publications

(145 citation statements)

References 13 publications

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“…A tabular comparison of the DISCover and UMd class lists can be found in Hansen et al (2000). The processing sequence for the IGBP-DISCover product was continent-by-continent as the data became available through the IGBP-DIS global 1 km project (Eidenshink and Faudeen 1994). The DISCover product was released via the World Wide Web ( http://edcwww.cr.usgs.gov/landdaac/glcc/glcc Õ na.html ) free of charge to all potential users in July 1997 and is scheduled to be improved annually based on peer-review, user feedback and results from the validation study.…”

Section: Methodological Similarities and DI Erencesmentioning

confidence: 99%

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

Hansen¹,

Reed²

2000

International Journal of Remote Sensing

263

139

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Abstract. 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 classi cation scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classi cation 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 classi cation scheme, while the UMd map employs a modi ed 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 di er signi cantly. 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 0.5ß 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 di culty in comparing the maps, due to its signi cant 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.

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Section: Methodological Similarities and DI Erencesmentioning

confidence: 99%

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

Hansen¹,

Reed²

2000

International Journal of Remote Sensing

263

139

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“…The maximum value of the normalized difference vegetation index (NDVI) computed from top-of-the atmosphere (TOA) radiances was used to select the most cloud-free pixel available for the period. Although other compositing algorithm options with better performance have been considered [Cihlar et al, 1994], maximum NDVI is still the most often used compositing criterion [e.g., Eidenshink and Faundeen, 1994;James and Kalluri, 1994] and has known biases. Only pixels with view zenith angles below 57 ø were eligible for compositing; this threshold was selected as a compromise between obtaining good temporal resolution and avoiding large off-nadir angles.…”

Section: Compositingmentioning

confidence: 99%

Seasonal AVHRR multichannel data sets and products for studies of surface‐atmosphere interactions

Cihlar¹,

Chen²,

Li³

1997

J. Geophys. Res.

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Abstract. A basic methodological premise in the design of the Boreal EcosystemAtmosphere Study (BOREAS) is that the findings and models obtained at the stand level can be applied at the landscape, regional and global levels by using spatially comprehensive data sets, in particular satellite observations and meteorological measurements. Since many of the processes of interest are strongly influenced by solar radiation, satellite measurements at optical wavelengths are of fundamental importance. We describe a satellite data set and derived products prepared for the studies of the ecosystem-atmosphere interactions, including the scaling up of site and landscape measurements, model development and validation, and many other applications. It is derived from daily measurements by the advanced very high resolution radiometer (AVHRR) onboard the NOAA 11 satellite. The data set was obtained through a compositing process to minimize the contamination by clouds. Subsequently, the ABC3 procedure [Cihlar et al., 1997] was applied to remove atmospheric attenutation effects, identify residual clouds or snow-covered pixels and remove the effect of this contamination, remove bidirectional reflectance effects, and correct for surface emissivity effects. The paper briefly reviews the correction procedures, discusses the characteristics of the corrected data set, and presents several derived products of biophysical parameters, including leaf area index and the fraction of photosynthetically active radiation, vegetation index accumulated over the growing season, and the daily total absorbed photosynthetically active radiation. Although the interactive surface-atmosphere processes ultimately take place at the molecular and cellular level, they must be characterized at larger spatial scales. This is necessary because most of the variability and dynamics of interest occurs at landscape, region, and global levels. It is at these larger scales that the impact of seasonal and interannual variability is most directly experienced by other components of the natural or anthropogenic environment. In the "scaling up," i.e., extending the knowledge and understanding of detailed process studies from small sites to the area beyond, satellite remote sensing provides the principal tool. Thus the second overarching objective of BOREAS was to "develop and validate remote sensing algorithms to transfer our understanding of the above pro-

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“…We then use the model to estimate aerial proportions of (1) woodiness (proportion woody vegetation, herbaceous vegetation, or bare ground), (2) leaf type (proportion of woody vegetation that is needleleaf or broadleaf), and (3) leaf longevity (proportion of woody vegetation that is evergreen or deciduous). We use data from the advanced very high resolution radiometer on board the NOAA operational meteorological satellites in this study, in particular, the 1 km AVHRR data set processed under the guidance of the International G½ospher½ Biosphere Program [Eidenshink and Faudeen, 1994 To reduce the possibilities of cloud contamination as well as to ease the problems of handling large volumes of data, we recomposited the 10-day composited images to monthly values based on the maximum NDV! value in the month [Holben, 1986].…”

Section: Requirements For Global Land Cover In Global Change Modelsmentioning

confidence: 99%

“…Running et al [1994bRunning et al [ , 1995 We describe a simple technique as an initial attempt to obtain continuous fields for three vegetation characteristics observable with remotely sensed data: growth form, leaf type, and leaf longevity. The fields are derived from the global 1-km resolution data set from the advanced very high resolution radiometer (AVHRR) [Eidenshink and Faudeen, 1994], serving as a prototype for continuous fields to be generated from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the EOS AM1 platform. MO-DIS will provide data with improved spatial resolution (250 m to 1 km, depending on the spectral band), spectral resolution, and atmospheric correction [Running et al, 1994a].…”

Section: Requirements For Global Land Cover In Global Change Modelsmentioning

confidence: 99%

Continuous fields of vegetation characteristics at the global scale at 1‐km resolution

DeFries¹,

Townshend²,

Hansen³

1999

J. Geophys. Res.

207

141

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Abstract. The geographic distribution of vegetation over the Earth's land surface is traditionally described using classification schemes with discrete numbers of vegetation types. When such land cover data sets are used as boundary conditions in Earth system models, abrupt boundaries and unrealistic homogeneity are introduced into parameter estimates. This paper proposes an alternative approach to describe global land cover with continuous fields of vegetation characteristics. A linear mixture model is applied to 1-km advanced very high resolution radiometer data to estimate proportional cover for three important vegetation characteristics: life form (percent woody vegetation, percent herbaceous vegetation, and percent bare ground), leaf type (percent needleleaf and percent broadleaf), and leaf duration (percent evergreen and percent deciduous).

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The 1 km AVHRR global land data set: first stages in implementation

Cited by 293 publications

References 13 publications

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

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

Seasonal AVHRR multichannel data sets and products for studies of surface‐atmosphere interactions

Continuous fields of vegetation characteristics at the global scale at 1‐km resolution

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