Supply-chain governance is needed to avoid deforestation

, but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE 016.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover. org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multitemporal depiction of Earth's tree cover available to the Earth science community.

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997-1999, (b) Syste`me pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961-2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992-1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space-time spiral curve (ST-SC) plots, (b) brightness-greenness-wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) 'zoom-in views' in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAMwas produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but 'equipped for irrigation' at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252Mha from season one, (b) 174Mha from season two and (c) 41Mha from continuous yea...

Accurate depiction of the land and water is critical for the production of land surface parameters from remote sensing data products. Certain parameters, including the land surface temperature, active fires and surface reflectance, can be processed differently when the underlying surface is water as compared with land. Substantial errors in the underlying water mask can then pervade into these products and any products created from them.Historically many global databases have been created to depict global surface water. These databases still fall short of the current needs of the terrestrial remote sensing community working at 250 m spatial resolution. The most recent attempt to address the problem uses the Shuttle Radar Topography Mission (SRTM) data set to create the SRTM Water Body Data set (SWBD 2005). The SWBD represents a good first step but still requires additional work to expand the spatial coverage to include the whole globe and to address some erroneous discontinuities in major river networks.To address this issue a new water mask product has been created using the SWBD in combination with MODIS 250 m data to create a complete global map of surface water at 250 m spatial resolution. This effort is automated and intended to produce a dataset for use in processing of raster data (MODIS and future instruments) and for masking out water in final terrestrial raster data products.This new global dataset is produced from remotely sensed data and provided to the public in digital format, free of charge. The data set can be found on the Global Land Cover Facility (GLCF) website at http://landcover.org. This dataset is expected to be a base set of information to describe the surface of Earth as either land or water which is a fundamental distinction upon which other descriptions can be made.