No abstract

Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site-level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite-based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty-five 1 km 2 cells at each site, with 8-day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at $ 30 m resolution), and process-based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m À2 yr À1 at an arctic tundra site to 550 g C m À2 yr À1 at a temperate deciduous forest site. There was not a consistent over-or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/ GPP algorithm -notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate datarevealed the causes of the over-and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project rotocol S for developing 25-m validation data layers over 49-km study areas. Results from comparisons of MODIS and BigFoot land cover and LA1 products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LA1 products, but there was not a particular MODIS LA1 algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LA1 was generally overpredicted by a significant amount. For evergreen needleleaf sites, LA1 seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed.

Human-wildlife conflicts, especially those involving large carnivores, are of global conservation and livelihood concern and require effective and locally-adapted prevention measures. Risk of lion attack on livestock (i.e., depredation) may vary seasonally and may be associated with variation in wild prey abundance or landscape characteristics. To test these competing hypotheses, we used a resource selection approach, and determined whether prey catchability (indicated by geo-spatial variables), or prey availability (indicated by modeled abundance recorded via camera traps) explained spatial and seasonal variation in livestock depredation risk by African lions on Manyara Ranch Conservancy, a multi-use area in northern Tanzania. Seasonal variation in vegetative productivity and proximity to surface water appeared to be strong predictors of livestock depredation risk. Correlates for depredation risk were different between wet and dry seasons. During the dry season, depredation risk was positively correlated with vegetative productivity, whereas depredation risk during the wet season was highest near livestock enclosures (bomas). During both seasons, depredation risk was high closer to surface water. Landscape-driven risk maps were created to identify low risk areas that may be compatible with livestock grazing. Our results on depredation risk by lions are similar to other studies in protected areas and suggest that both prey catchability and prey availability are instrumental in predicting kill sites of lions. To facilitate lion and livestock coexistence in multi-use areas of Africa, we recommend minimizing spatiotemporal overlap between livestock and abundant wild prey by developing alternative livestock water and feeding locations and increasing caution near surface water areas.