Until recently, advanced very high-resolution radiometer (AVHRR) observations were the only viable source of data for global land cover mapping. While many useful insights have been gained from analyses based on AVHRR data, the availability of moderate resolution imaging spectroradiometer (MODIS) data with greatly improved spectral, spatial, geometric, and radiometric attributes provides significant new opportunities and challenges for remote sensing-based land cover mapping research. In this paper, we describe the algorithms and databases being used to produce the MODIS global land cover product. This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP. To generate these maps, a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data. In addition to the IGBP class at each pixel, the MODIS land cover product provides several other parameters including estimates for the classification confidence associated with the IGBP label, a prediction for the most likely alternative class, and class labels for several other classification schemes that are used by the global modeling community. Initial results based on 5 months of MODIS data are encouraging. At global scales, the distribution of vegetation and land cover types is qualitatively realistic. At regional scales, comparisons among heritage AVHRR products, Landsat TM data, and results from MODIS show that the algorithm is performing well. As a longer time series of data is added to the processing stream and the representation of global land cover in the site database is refined, the quality of the MODIS land cover product will improve accordingly.
This paper describes the GTAP land use data base designed to support integrated assessments of the potential for greenhouse gas mitigation. It disaggregates land use by agro-ecological zone (AEZ). To do so, it draws upon global land cover data bases, as well as state-of-the-art definition of AEZs from the FAO and IIASA. Agro-ecological zoning segments a parcel of land into smaller units according to agro-ecological characteristics, including: precipitation, temperature, soil type, terrain conditions, etc. Each zone has a similar combination of constraints and potential for land use. In the GTAP-AEZ data base, there are 18 AEZs, covering six different lengths of growing period spread over three different climatic zones. Land using activities include crop production, livestock raising, and forestry. In so doing, this extension of the standard GTAP data base permits a much more refined characterization of the potential for shifting land use amongst these different activities. When combined with information on greenhouse gas emissions, this data base permits economists interested in integrated assessment of climate change to better assess the role of land use change in greenhouse gases mitigation strategies.
The paper describes the on-going project of the GTAP land use data base. We also present the GTAPE-AEZ model, which illustrates how land use and land-based emissions can be incorporated in the CGE framework for Integrated Assessment (IA) of climate change policies. We follow the FAO fashion of agro-ecological zoning (FAO, 2000; Fischer et al, 2002) to identify lands located in six zones. Lands located in a specific AEZ have similar (or homogenous) soil, landform and climatic characteristics. The six AEZs range over a spectrum of length of growing period (LGP) for which their climate characteristics can support for crop growing. AEZ 1 covers the land of the temperature and moisture regime that is able to support length of growing period (LGP) up to 60 days per annum. On the other end of the LGP spectrum, lands in AEZ 6 can support a LGP from 270 to 360 days per annum. Crop growing, livestock breeding, and timber plantation are dispersed on lands of each AEZ of the six, whichever meets their climatic and edaphic requirements. In GTAPE-AEZ, we assume that land located in a specific AEZ can be moved only between sectors that the land is appropriate for their use. That is, land is mobile between crop, livestock and forestry sectors within, but not across, AEZ’s. In the
Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1 km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends. As a result, comparison of the different land cover maps is difficult and information about their relative utility is limited. In an attempt to compare the datasets and assess their strengths and weaknesses we harmonized the thematic legends of four available coarse-resolution global land cover maps (IGBP DISCover, UMD, MODIS 1-km, and GLC2000) using the LCCS-based land cover legend translation protocols. Analysis of the agreement among the global land cover maps and existing validation information highlights general patterns of agreement, inconsistencies and uncertainties. The thematic classes of Evergreen broadleaf trees, Snow and Ice, and Barren show high producer and user accuracy and good agreement among the datasets, while classes of mixed tree types show high commission errors. Overall, the results show a limited ability of the four global products to discriminate mixed classes characterized by a mosaic of trees, shrubs, and herbaceous vegetation. There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes. Suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes.
Two of the most widely used land-cover data sets for the United States are the National Land-Cover Data (NLCD) at 30-m resolution and the Global Land- Cover Characteristics (GLCC) at 1-km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land-cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1-km unit over a total of 11 agricultural-related eco-regions across the continental United States. Our results exhibited that data agreement or relationship between the GLCC and NLCD was higher for the eco-regions located in the corn belt plains with homogeneous or less complicated land-cover distributions. The GLCC cropland primarily corresponded to NLCD row crops, pasture/hay and small grains, and was occasionally related to NLCD forest, grassland and shrubland in the remaining eco-regions due to high land-cover diversity. The unique GLCC classes of woody savanna and savanna were mainly related to the NLCDorchard and grassland, respectively, in the eco-region located in the Central Valley of California. The GLCC urban/built-up among vegetated areas strongly agreed to the NLCD urban for the eco-regions in the corn belt plains. A set of subclass land-cover information provided through this study is valuable to understand the degrees of spatial similarity for the major global vegetated classes. The subclass information from this study provides reference for substituting less-detailed global data sets for detailed NLCD to support national environment studies.