Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Abstract

produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes de ned to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-bycontinent unsupervised classi cation of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Di erence Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classi cation strati cation was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover

Interannual variability of surface water extent at the global scale, 1993–2004

“…The technique is globally applicable without any calibration for individual environments . Note that inland seas (Caspian Sea, Aral Sea), large lakes such as the Great Lakes in North America, for instance, and coastal pixels that are likely contaminated by the ocean are suppressed from the results , using the IGBP 1-min land ecosystem classification map [Loveland et al, 2000]. [18] The use of active microwave observations in the estimation of surface water extent is important because it helps identify inundated pixels and is used in the linear mixture model to account for the effects of vegetation cover.…”

“…Prigent et al [2007] presented results over 8 years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000), and monthly results of spatial and temporal dynamics of inundations were analyzed and evaluated for different environments by comparison with in situ measurements and other related remote sensing observations. Compared with different independent static data sets, each representing subsets of the inundation extent from the multisatellite technique (including the wetlands distribution from Matthews and Fung [1987], the rice paddies distribution from Matthews et al [1991], and the inland permanent water from the International Geosphere-Biosphere Programme (IGBP) [Loveland et al, 2000]), the results showed very realistic global distributions for all latitudes and environments, with, however, a limitation of the multisatellite approach in detecting most small inland water bodies that cover less than 10% of a pixel (a pixel covers 773 km 2 ).…”

Characteristics of MODIS land-cover data sets over Northeast Asia for the recent 12 years(2001-2012)

“…기후변화 영향 평가, 지면-대기 상호작용 등 다양한 분야에서 요구되는 토지피복 분류는 주로 National Oceanic and Atmospheric Administration (NOAA), Terra/ Aqua 등 극궤도위성 자료로부터 산출된 식생지수 (Normalized Difference Vegetation Index, NDVI)를 이용한 다 (Loveland et al, 2000;Friedl et al, 2010). 식생지수는 상 대적인 식생의 양과 상태를 지수로 나타낸 것으로써 식…”

“…Loveland et al, 2000;Bartholomé and Belward, 2005;Bontemps et al, 2010;Friedl et al, 2010), 국 내에서도 수치모델이나 지역기후 모델, 지면상태 변화 탐지 등에 적합한 토지피복 분류 연구가 수행되어 왔다 (Suh et al, 2001;Jeong et al, 2006;Han, 2008;Kang et al, 2009;Kang et al, 2010). 하지만 현재 제공되고 있는 다양 한 토지피복 자료들 사이에는 위성의 센서, 공간해상도, 초기사용자료, 전처리 수준, 분류 기법 등의 차이로 적지 않은 화소들에서 분류가 상이하게 되어 있음이 밝혀지 고 있다 (McCallum et al, 2006;Kang et al, 2007;Herold et al, 2008;Tchuenté et al, 2011).…”

Locating poor livestock keepers at the global level for research and development targeting

“…In mapping the classification, we used human population densities (described briefly above), the United States Geological Survey's Land Use/Land Cover System database and legend (Anderson et al, 1976;Loveland et al, 2000), length-of-growing-period surfaces (Fischer et al, 2000;Jones, 1987;Jones and Thornton, 1999;IWMI, 1999), a global coverage of the irrigated areas (D . oll and Siebert, 2000;Siebert and D .…”