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Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.

Plasmonic enhanced upconverting nanoparticles were constructed for visible to near-infrared light-driven photo-polymerization.

Aim Although the influence on species richness of landscape attributes representing landscape composition and spatial configuration has been well documented at landscape scales, its effects remain little understood at macroecological scales. We aim to assess the role of landscape attributes, and their relative importance compared with climate, habitat heterogeneity and human influence (CHH) in particular, in shaping broad-scale richness patterns. Location Mainland China.Methods Species richness data for mammals, birds, reptiles and amphibians were derived from the China Species Information Service. Together with the richness data, CHH variables and class-and landscape-level landscape metrics were calculated using grain sizes of 50 km × 50 km, 100 km × 100 km and 200 km × 200 km. At these multiple scales, the species richness of each taxonomic group was correlated with CHH and landscape variables using both ordinary least square (OLS) and simultaneous autoregressive (SAR) models; variation partitioning was used to assess the relative strength of landscape attributes versus CHH variables. ResultsIn general, climate is the most influential factor shaping richness patterns. Landscape attributes, especially class-level attributes, can also explain considerable variation in richness. Variation partitioning showed largely overlapped fractions of explained variation between landscape attributes and CHH variables. The pure explanatory power of landscape attributes was small for mammals, reptiles and amphibians, showing R 2 of 1-3%, while it was considerably larger for birds, showing R 2 of 5-10%. The environment-richness correlations showed scale dependency, but the pure explanatory power of landscape attributes appeared to show small changes across the scale range used in this study.Main conclusions In addition to CHH variables, landscape attributes can explain some broad-scale richness patterns, especially for birds. The incorporation of landscape attributes will be conducive to better understanding the drivers of richness patterns and modelling species richness at macroecological scales.