- Linking vegetation heterogeneity and functionaL attributes of temperate grassLands - 117 Applied Vegetation Science 11: 117-130, 2008 doi: 10.3170/2007-7-18429, published online 14 December 2007 © IAVS; Opulus Press Uppsala. Abstract Question: How are plant communities of the Flooding Pampa grasslands spatially distributed? How do canopy dynamics of the different communities vary among seasons and years? Location: Buenos Aires province, Argentina. Methods: We characterized the distribution of communities through a supervised classiication based on four Landsat 5 TM images. We sampled species composition of 200 sites, with 130 of them corresponding to natural communities. Of the sampling areas 60% were used to classify, and the remaining areas to assess classiication accuracy. We characterized the seasonal and interannual variability of canopy dynamics using NDVI (Normalized Difference Vegetation Index) data provided by MODIS /Terra images. Results: Overall accuracy of the classiication was satisfactory. The resulting maps showed a landscape formed by a matrix of extended lowlands with small patches of mesophytic and humid mesophytic meadows. The October scene (near the peak of productivity) was particularly important in discriminating among communities. The seasonal pattern of NDVI differed among communities and years. Mesophytic meadows had the highest NDVI mean and the lowest interannual coeficient of variation, halophytic steppes had the lowest mean, and veg- etated ponds were the most variable. Conclusions: These grasslands have a ine-grained heterogene- ity at the landscape scale. Each plant community has distinct seasonal and interannual canopy dynamics. These two features of grassland structure and functioning represent key informa- tion for rangeland management that may be obtained through a combination of minor ield sampling and remote sensing. Keywords: Canopy dynamics; Land-cover classiication; Landsat 5 TM; Landscape heterogeneity; MODIS; NDVI. Abbreviations: A = Mesophytic meadow; ANPP = Above- ground net primary productivity; B = Humid mesophytic meadows; C = Humid prairies; D = Halophytic steppes; EVI = Enhanced Vegetation Index; (f)APAR = (fraction of) Absorbed photosynthetically active radiation; LAI = Leaf area index; NDVI = Normalized difference vegetation index; PAR = Photosynthetically active radiation; RMS = Root mean square; RUE = Radiation use eficiency; VP = Vegetated pond. Linking vegetation heterogeneity and functional attributes of temperate grasslands through remote sensing Aragón, Roxana 1* & Oesterheld, Martín 2 1 IFEVA, Cátedra de Métodos Cuantitativos Aplicados, Facultad de Agronomía, Universidad de Buenos Aires, Av. San Martín 4453, 1417 Buenos Aires, Argentina; 2 IFEVA, Cátedra de Ecología, Facultad de Agronomía, Universidad de Buenos Aires, Argentina; E-Mail oesterheld@ifeva.edu.ar; * Corresponding author; Fax+54 1145148730; E-mail aragon@ifeva.edu.ar Introduction Knowing the spatial arrangement of land cover types is essential for resource management, land-use planning and biodiversity conservation (Eyre et al. 2003; Ruiz- Luna & Berlanga-Robles 2003; Venier et al. 2004). In areas where natural vegetation is subjected to commercial production activity, such as extensive grazing, the spatial variability of natural resources may be inferred by map- ping plant communities (Paruelo et al. 2000; Cingolani et al. 2004). However, from an applied perspective, it is necessary to take a step forward, and describe the sea- sonal and interannual variation of functional attributes, such as canopy dynamics or productivity. This integrated approach may be useful for designing sustainable strate- gies which, due to ranch and paddock sizes, are often taken at this scale. Mapping plant communities and knowing the sea- sonal and interannual variation of their productivity at the landscape scale, is dificult because of the sampling effort required to get spatially explicit, detailed results. Land cover classiication is often performed at global or regional scales (Fairbanks & McGwire 2004; Paruelo et al. 2004) and information about primary productivity frequently relies on biomass collection studies performed at patch scale (Sims et al. 1978; Scurlock et al. 2002). Biomass collection presents advantages and limita- tions (Singh et al. 1975; Sala et al. 1988; Sala & Austin 2000), but it is always time demanding and frequently unfeasible at large spatial or long temporal scales. Due to these constraints, accurate spatial information about community distribution and their functional attributes at the landscape scale is often lacking. Satellite remote sensing provides a solution to these two issues of land cover classiication and description of seasonal and interannual patterns of productivity. Regard- . Regard- ing land cover classiication, the increasing availability of satellite remote sensing information allows character- ization of land cover spatial distribution over different geographic scales with a comprehensive temporal cov- erage (Vogelmann et al. 2001; Alvarez et al. 2003; Kerr & Ostrovsky 2003). Remote sensing can be particularly