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Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria
(2022)
The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.
Animal pollinators are globally threatened by anthropogenic land use change and agricultural intensification. The yield of many food crops is therefore negatively impacted because they benefit from biotic pollination. This is especially the case in the tropics. For instance, fruit set of Coffea arabica has been shown to increase by 10–30% in plantations with a high richness of bee species, possibly influenced by the availability of surrounding forest habitat. Here, we performed a global literature review to (1) assess how much animal pollination enhances coffee fruit set, and to (2) examine the importance of the amount of forest cover, distance to nearby forest and forest canopy density for bee species richness and coffee fruit set. Using a systematic literature review, we identified eleven case studies with a total of 182 samples where fruit set of C. arabica was assessed. We subsequently gathered forest data for all study sites from satellite imagery. We modelled the effects of open (all forest with a canopy density of ≥25%), closed (≥50%) and dense (≥75%) forests on pollinator richness and fruit set of coffee. Overall, we found that animal pollination increases coffee fruit set by ~18% on average. In only one of the case studies, regression results indicate a positive effect of dense forest on coffee fruit set, which increased with higher forest cover and shorter distance to the forest. Against expectations, forest cover and distance to open forest were not related to bee species richness and fruit set. In summary, we provide strong empirical support for the notion that animal pollinators increase coffee fruit set. Forest proximity had little overall influence on bee richness and coffee fruit set, except when farms were surrounded by dense tropical forests, potentially because these may provide high-quality habitats for bees pollinating coffee. We, therefore, advocate that more research is done to understand the biodiversity value of dense forest for pollinators, notably assessing the mechanisms underlying the importance of forest for pollinators and their pollination services.