# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## Material and Method
### Climate and Satellite Data
The _average_ predictor is a proxy of landscape productivity and the dominant land cover in the plot. The _standard deviation_ reflects landscape heterogeneity. To examine whether seasonal variations effect of NDVI on the models performance depends on the landscape context, we used land cover/land use datasets to calculate landscape composition  
Figure 2: Image data used for the study: five NDVI 16-day-composite products at different time periods in 2010 (250-m spatial resolution) and one map of climate regions based on [31]. in each bird plot. The forest layer was obtained from a nationwide highly detailed vector spatial database (BDTopo(r), French mapping agency IGN) at 1-m resolution. For agricultural areas, the graphic parcel register (RPG) of 2010 was used. The French RPG database is produced annually since 2002 in application of the EU directives related to the Common Agricultural Policy. The land use information, based on a 28 class nomenclature (including several types of crops, as well as artificial and natural grasslands), is obtained from the farmers' declaration of their cultivated areas. The information is defined at the agricultural block level composed of one single field or several contiguous ones. For each block, the proportion of each crop type is provided but without the spatial distribution of crops within the block. The Dynamic Habitat Index of 2010 was also calculated at the national scale, in order to compare the performance of the DHI-based model with the ones based on single date NDVI. The DHI was derived from the most detailed MODIS fPAR (fraction of Photosynthetically Active Radiation) 8-day composite product at 500-m spatial resolution (MOD15A2H) [34]. It includes three components [26]: (1) the cumulative annual fPAR (DHI cum) reflecting the overall potential of vegetation productivity; (2) the minimum annual fPAR value (DHI min) indicating the minimum amount of vegetated cover; and (3) the coefficient of variation of annual fPAR (DHI cv) expressing the seasonality in productivity (Figure 3). The _average_ and _standard deviation_ of the three DHI components were calculated for each plot of bird data (\(n=759\)) and used as dependent variables. All the variables were retained in the bird-habitat models after checking correlations between them (Spearman's \(|\,Rho\,|<0.7\)). In order to avoid bias in the comparison with NDVI-based models because of difference in spatial resolution, NDVI variables were also computed from the MODIS product at 500-m (MOD13A1). To evaluate the seasonal effect of NDVI on models's performance independently of DHI (see above), we kept the most detailed NDVI data at 250-m. This spatial resolution is likely to produce models with higher explanatory and predictive performances [21]. Figure 3: The components of the Dynamic Habitat Index of 2010 over France derived from the MODIS fPAR 8-day composite product at 500-m spatial resolution: (**a**) cumulative productivity; (**b**) minimum productivity; (**c**) intra-annual variation of productivity expressed as the coefficient of variation; (**d**) the composite DHI. The components were normalized from 0 to 1 for visualization.